Joint Statistical Meetings 2009

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It was several weeks before JSM 2007 when I got the email– “Was I interested in being the JSM 2009 ProgramChair?” I immediately replied with a resounding YES! Myname and others were then submitted as potential chairs tothe Committee on Meetings. The committee chose me as the2009 Program Chair, and my election was subsequentlyapproved by Sally Morton, our current ASA President, andthe ASA Executive Board.

One of my first tasks in late 2007 was to appoint two GeneralMethods Chairs and the Poster Chair. These folks are veryimportant members of the program committee because theyhelp me in many ways, such as with organizing the program,filling in for other committee members, and more. The remaining members of theprogram committee are either appointed by a member society (e.g., IMS, SSC,etc.) or elected by their section.

Around this same time, ASA President Morton picked the theme for our meeting,and she chose an exciting one – Statistics: From Evidence to Policy. While sessionsdo not have to address it, the theme is very important, as it tends to drive the con-tent of the invited and topic-contributed sessions.

To gain experience in the process, the JSM 2009 Program Committee had toarrange some events for the previous year’s program. For example, the committeewas responsible for organizing and scheduling the roundtable coffees and lunchesfor the 2008 meetings. We also started working on the invited program during thissame time period – one year before our JSM!

Then, the hard (but exciting and fun!) work begins. The partner soci-eties, the ASA sections, and other entities have a certain number of

invited sessions allocated to them. The appropriate committeemembers can organize the sessions or solicit ideas from their

members. The representatives for the ASA sections, com-mittees, and interest groups are allowed to submit two

invited sessions for the competition slots, and theentire program committee votes on the ones they

think should be included in the program. Thiswas certainly a challenging and important part

of the process because the invited sessionsare the backbone of the meetings.

Fit linearmodels without

intercepts in SAS

or R

Making the Most Out of those Complex Datasetsin the Social and Behavioral Sciences

StatisticsNewS Nexus

Summer 2009

JSM 2009 – A Behind the Scenes Look

SASproc glm data=ds;

model y = x1 ... xk / noint;

run;

Note: The noint option works with many modelstatements.

Rmod1 < –lm(y ~ 0 + x1 + ... + xk,data=ds)

or

mod1 < –lm(y ~ x1 + ... + xk –1,data=ds)

Large and complex datasets are becoming increasingly prevalent in the social andbehavioral sciences. The Chapman & Hall/CRC Statistics in the Social and BehavioralSciences Series makes the lives of statisticians working in the applied sciences easier byexploring the effective and efficient use of statistical, econometric, and psychometricmethods. Two new books in the series explore the latest methods and tools in thisrapidly growing area.

Emphasizing causation as a functional relationship between variables that describeobjects, Linear Causal Modeling with Structural Equations (2009), by Stanley A.Mulaik, integrates a general philosophical theory of causation with structural equationmodeling that concerns the special case of linear causal relations. In addition todescribing how the functional relation concept may be generalized to treat probabilis-tic causation, the book reviews historical treatments of causation and explores recentdevelopments in experimental psychology on studies of the perception of causation. InMultivariable Modeling and Multivariate Analysis for the Behavioral Sciences(2009)Brian S. Everitt goes beyond the usual coverage of hypothesis testing to provide a fullexplanation of the most useful methods for comparing and examining data in psychol-ogy, the social sciences, and law. He includes real data sets that provide a sampling ofthe issues commonly encountered when applying statistics in practice.

In partnership with Stata Press, Chapman Hall/CRC offers Microeconometrics UsingStata (2009), co-authored by A. Colin Cameron and Pravin K. Trivedi, provides an out-standing introduction to microeconometrics and microeconometric research usingStata. Cameron and Trivedi cover specialized topics often left out of microeconomet-rics texts and basic introductions to Stata. They address each topic with an in-depthStata example and demonstrate how to use Stata’s programming features to implementmethods for which Stata does not have a specific command.

Although interest in spatial regression models has surgedin recent years, a comprehensive, up-to-date text onthese approaches does not exist. Rounding outthe collection, Introduction to SpatialEconometrics (January 2009), presents a vari-ety of regression methods used to analyzespatial data samples that violate the tradi-tional assumption of independencebetween observations. James LeSage andR. Kelley Pace explore recent advancesin spatial econometric models—includ-ing some previously unpublishedresults.

A. Colin Cameron joins the distinguished panel of series editors that include Andrew Gelman,J. Scott Long, Sophia Rabe-Hesketh, and Anders Skrondal. Dr. Cameron is a professor in the

Department of Economics at the University of California, Davis and has served as theDirector of the U.C. Davis Center on Quantitative Social Science Research.

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Routines for linear models in SAS andR incorporate an intercept by default.But, what's the syntax for fitting amodel without an intercept? This entryfrom SAS and R: Data Management,Statistical Analysis, and Graphics(July 2009) will tell you.

The book is designed as a bilingualSAS/R dictionary: If you know SAS,you can look up the noint option in theSAS index. If you know R, you canlook up the 0 operator, or if you'reunaware of how to do it in either, youcan look up no intercept in the subjectindex.

story continued on pg. 7 (JSM 2009)

JSM 2009 - Washington, DCFrom Evidence to Policy

Wendy L. MartinezJSM 2009

Program Chair

David J. Hand is a professor of statisticsat Imperial College in London, whereamong his diverse areas of responsibility,he is chair of Statistical Methods in theRetail Financial Services ResearchGroup. Dr. Hand serves as president ofthe Royal Statistical Society and is aFellow of the British Academy. He has

been a recipient of the Guy Medal of the Royal StatisticalSociety, the Royal Society Wolfson Research MeritAward, and the IEEE ICDM Research ContributionsAward. He has published countless papers and more than20 books, including the newly available ROC Curvesfor Continuous Data (May 2009), with co-author Wojtek J. Krzanowski. He alsolaunched the journal Statistics andComputing and served a term as editor ofthe Journal of the Royal Statistical Society,Series C. Dr. Hand’s broad researchinterests include multivariate statis-tics, classification methods, patterndetection, and the interface betweenstatistics and computing. His influenceand advice has led to improvements inorganizations ranging from banks to phar-maceutical companies to governments.

A key member of the development team forthe R and Omegahat projects, Paul Murrell isone of the foremost innovators of the R envi-ronment. Focusing his research on statisticalcomputing and graphics, Dr. Murrell is theauthor of the bestselling R Graphics (2005)and the recently published Introduction toData Technologies (February 2009). He is asenior lecturer at the University of Auckland. It was atAuckland that he came under the influence of RobertGentleman and Ross Ihaka—the noted R & R team that orig-inated the R environment—and found his path. “When I

found myself writing my own plotting software to producecharts for a psychology project report, I took it as an

indicator of where my interests really lay.”Currently, XML and its vast research potential

has Dr. Murrell’s attention. As for the futureof statistics, he says, “From a research per-spective, developing software tools needs tobe a recognized career path in statistics sothat people consider it as an option and arerewarded for making that career choice.From an educational perspective, statistics

curriculums need to formally address topicsin statistical computing, so that people have

the necessary skills to be able to engage in soft-ware research.” Those lucky enough to assist him

or attend his lectures have ample opportunity to wit-ness both those perspectives in practice.

2

StatisticsNewS What’s News StatisticsNewS PioneersPlotting the Future

SATURDAY, AUGUST 1

Missing Data in Longitudinal Studies: Strategies for BayesianModeling and Sensitivity Analysis- Continuing Education Course CE_02C! Michael J. Daniels and Joseph W. Hogan authors of Missing Data

in Longitudinal Studies: Strategies for Bayesian Modeling andSensitivity Analysis teach this course.

SUNDAY, AUGUST 2

Tolerance Intervals: Theory, Applications, and Computation- Continuing Education Course CE_10C! Presented by Kalimuthu Krishnamoorthy, author of Handbook of

Statistical Distributions with Applications with Thomas Mathew. Hierarchical Modeling and Analysis of Spatial-Temporal Data:Emphasis in Forestry, Ecology, and Environmental Sciences- Continuing Education Course CE_12C! Sudipto Banerjee and Andrew Finley give a short course based on

Banerjee’s book: Hierarchical Modeling and Analysis for SpatialData.

Recent Advances in Bayesian Model Selection Invited Papers 3! David Madigan, editor of the Computer Science and Data Analysis

series, discusses Sequential Bayesian Model Selection.Introductory Overview Lecture: Largely About Largeness:Models And Views For High-Dimensional Data 41! Organized by Wendy Martinez, co-author of the forthcoming

Exploratory Data Analysis with MATLAB, Second Edition.The Issue of High Dimensionality and Missing Data in ComplexEpidemiological Studies Invited Papers 46! Nicholas P. Jewell, author of Statistics for Epidemiology, presents

Estimating and Ranking the Impact of High-Dimensional EnvironmentalRisk Factors In Environmental Epidemiology.

MONDAY, AUGUST 3

Dose Finding Studies: Methods and Implementation- Continuing Education Course CE_16C! Frank Bretz, co-author of Multiple Comparisons Using R teaches

this course with Jose C. Pinheiro.IMS Medallion Lecture II Invited Papers 96! Chaired by Marina Meila, co-editor of the Computer Science and

Data Analysis series.Recent Advances in Missing Data and Causal Inference InvitedPapers 131! Geert Molenberghs, co-author of Longitudinal Data Analysis dis-

cusses Every Missing Not At Ranom Model for Incomplete Data HasGot a Missing At Random Counterpart With Equal Fit. Joseph L.Schafer, author of Analysis of Incomplete Multivariate Data,offers Causal Modeling When the Treatment is a Latent Class.

JASA, Theory and Methods Invited Session Invited Papers 140! Raymond J Carroll, co-author of Measurement Error in

Nonlinear Models: A Modern Perspective, Second Editionpresents Prediction in Measurement Error Models with AuroreDelaigle. Jianqing Fan, co-editor of the Monographs on Statistics and Applied Probability series and Susan Schennah are discussants inthis session.

Recent Advances and the Future of Statistics Invited Papers198! Brad Efron, co-author of An Introduction to the Bootstrap, exam-

ines The Future of Indirect Evidence. TUESDAY, AUGUST 4

Monte Carlo and Bayesian Computation with R - Continuing Education Course CE_25C! Maria Rizzo, author of Statistical Computing with R, instructs this

course with James Albert.Comprehensive Data Analysis Using Interactive StatisticalGraphics - Continuing Education Course CE_26C! Martin Theus and Simon Urbanek present a short course based on

their book Interactive Graphics for Data Analysis: InteractiveGraphics for Data Analysis.

Advances in Spatial and Spatio-Temporal Modeling InvitedPapers 246! Christopher Wikle, co-editor of the Interdisciplinary Statistics series,

presents A Class of Nonlinear Spatio-Temporal Dynamic Models.IMS Medallion Lecture IV Invited Papers 250! Chaired by Galin L. Jones, co-author of Handbook of Markov

Chain Monte Carlo: Methods and Applications.Emerging Trends and Evidence in High-Dimensional "Omics"investigations Invited Papers 290! David B. Allison, co-editor of DNA Microarrays and Related

Genomics Techniques: Design, Analysis, and Interpretation ofExperiments, explores Biostatistics' Rise As an Empirical Science:Genomic Research as the Leavening Agent.

Random Effect Specification/Misspecification in Spatial/Spatio-temporal Health Modeling Invited Papers 291! Organized by Andrew B. Lawson, author of Bayesian Disease

Mapping: Hierarchical Modeling in Spatial Epidemiology.WEDNESDAY, AUGUST 5

Bayesian Methods in Medicine and Biology Invited Papers 448! Organized by Wesley O. Johnson, co-author of Bayesian Ideas and

Data Analysis: An Introduction for Scientists and Statisticians.Statistics in Defense and National Security Roundtable withLunch 487! Organized by Jeffrey L. Solka, co-author of Exploratory Data

Analysis with MATLAB, Second Edition.THURSDAY, AUGUST 6

Introductory Overview Lecture: Statistical Learning and DataMining 535! David J. Hand, co-author of ROC Curves for Continuous Data,

explores Statistical Learning and Data Mining.The Influence of Psychology, Cartography and ComputerScience on the Design of Interactive Graphics for SpatialStatistical Data Invited Papers 540! Organized by Linda Williams Pickle, co-author of Visualizing Data

Patterns with Micromaps with Daniel B. Carr. Carr presentsVisualizing Patterns in Data with Micromaps in this session.

2 77

! ! ! JSM HIGHLIGHTS ! ! !

There are also several invited session categories that are cho-sen at the discretion of the JSM Program Chair, such as ASAjournals, outside societies, ASA committees, memorial ses-sions, and Introductory Overview Lectures. The GeneralMethods Chairs and I also had someinvited session slots that we could fill.Thus, we could somewhat shape theprogram via these selections.

Most of the invited program was final-ized by December 2008, after which weturned our attention to the contributedprogram. Proposed invited sessions that did not make it on theprogram could be converted to topic contributed sessions.However, for the most part, the JSM Program Committee justhad to wait for contributed abstracts to roll in, which they did.We received approximately 500 more abstracts (on average)than the previous two meetings! Needless to say, this present-ed some significant scheduling challenges to the ASAMeetings Department and the JSM Program Committee, andeveryone did a great job handling these and many other issues.

You probably realize by now that I had a lot of help puttingthe program together, and I would like to take this opportu-nity to acknowledge the help of many people. First, I wouldlike to thank our ASA President, Sally Morton for her excel-

lent leadership and support. The JSMProgram Committee, the GeneralMethods Chairs, and the Poster Chairdid the bulk of the work organizing theprogram. In particular, our JSM PosterChair, Lara Schmidt did an outstand-ing job putting together and promoting

poster sessions. We could not do anything without the won-derful people at the ASA Meetings Department: DonnaArrington, Kathleen Wert, and Steve Porzio. I want to say aspecial thank you to Donna, who was responsible for the JSMtechnical program. She was always helpful, cheerful, andpatient with our many questions and issues. Of course, theJSM could not happen without the participation of the pre-senters, chairs, organizers, exhibitors, and all attendees – sothank you for making this JSM an event to remember!

JSM 2009 (continued from pg. 1)

I think that this is one of the important challenges that all members of the programcommittee faced – organizing a balancedprogram and not letting our biases and

interests motivate our choices.

By now, most statisticians recognize R as one of themost flexible, integrated software environments forstatistical computing and graphics. Its exceptionalcapacity for adaptability and its sophisticated presen-tation features make it the first choice of experiencedstatisticians as well as researchers with minimal formaltraining in statistical software. Chapman & Hall/CRCPress offers an array of books to help readers at variouslevels understand R and make use of its wide range ofapplications.

Like its bestselling predecessor, A Handbook of Statistical Analyses Using R,Second Edition (August 2009) continues to offer straightforward descriptions of how touse R and interpret the results. Authors Brian S. Everitt and Torsten Hothorn includenew chapters on graphical displays, generalized additive models, and simultaneousinference, as well as a new section on generalized linear mixed models. In SAS and R:Data Management, Statistical Analysis, and Graphics (July 2009), Ken Kleinman andNicholas J. Horton present an easy way to learn how to perform an analytical task inboth SAS and R, without having to navigate through the extensive, idiosyncratic, andsometimes unwieldy software documentation. Introduction to Data Technologies(March 2009), written by R guru Paul Murrell, a member of the R Development CoreTeam, provides important information on how to work with research data. Murrelldevotes separate chapters to each computer language, including R.

Hidden Markov Models for Time Series: An Introduction Using R (April 2009),authored by Walter Zucchini and Iain L. MacDonald demonstrates how to applyHMMs to a wide range of time series types and discusses how to employ R to carry outcomputations for parameter estimation, model selection and checking, decoding, andforecasting. In Computational Statistics: An Introduction to R (January 2009),Günther Sawitzki also illustrates how R can be employed to tackle statistical problems.

Unlike most guides to R, Introduction to Scientific Programming and SimulationUsing R (March 2009) by Owen Jones, Robert Maillardet, and Andrew Robinson doesnot focus on the application of statistical techniques, but rather shows how to turnalgorithms into code. This introductory book surveys a range of useful mathematicaltools that either require or are illuminated by scientific computation. Another intro-ductory text, An Introduction to Statistical Inference and Its Applications with R(June 2009) by Michael W. Trosset, explains how statistical methods are used for dataanalysis and uses the elementary functions of R to perform the individual steps of sta-tistical procedures.

Most of these books offer code and supplemental material online. For more detailsabout any of these, or other related works, please visit www.crcpress.com.

The analysis of genetic data hasbecome a science unto itself, withstatisticians taking on a critical role.Three new titles from CRC Presscapture the state of the science inthis fascinating field.

Focusing on problems arising in theanalysis of genetics and genomics,Statistics in Human Genetics andMolecular Biology (July 2009) pro-vides a graduate textbook of statisti-cal methods used in genetics.Author Cavan Reilly covers clusteranalysis, combinatorial optimiza-tion, and dynamic programming,along with the core topics of genomemapping, biological sequence analy-sis, and the analysis of gene expres-sion arrays. He also exploresBayesian approaches, such as hiddenMarkov models and black motifmethods, along with modern tools ofBayesian analysis, including Markovchain Monte Carlo (MCMC).

With the diversity of data and meta-data now available, there isincreased interest in analyzing mul-tiple studies beyond statisticalapproaches of formal meta-analysis.Meta-analysis and CombiningInformation in Genetics andGenomics (July 2009) is the firstbook devoted to meta-analysis ingenetics. Addressing the complica-tions inherent in comparing andreplicating genetic studies, leadingexperts consider microarrays, genemapping, and proteomics whiledetailing the various techniquesused to analyze and combine datafrom different types of studies.

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66 33

Are you tapping into all that R offers?Taylor & Francis Journals— online and in print

One of the best tools forgene expression studies is theAffymetrix GeneChip®. Thisplatform is the most broadlyadopted single-color tech-nology used in academic labs, clinics, and commercial entities. GeneExpression Studies Using Affymetrix Microarrays (CRC Press, June 2009) pro-vides a detailed and systematic introduction as well as a practical user’s guide tothis promising technology, including the use of R and other software formicroarray analysis. The text also includes a wealth of information and key ter-minology regarding microarray gene expression studies in general.

Communications in Statistics: Simulationand Computation and Communications inStatistics: Theory and Methods includespapers on advance theory and methods inthe computational aspects of Probabilityand Statistics. This pair of related journals,edited by N. Balakrishnan, presents formu-lation and discussion of problems as well assolutions in all areas of statistics, includinggraphic methods, resampling, and compu-tationally intensive methods. In addition,the journals provide exhaustive coverageand up-to-date reviews of key topics bypublishing special issues.

Sequential Analysis, edited by NitisMukhopadhyay, contributes to theoreticaland applied aspects of sequential andapplied methodologies in all areas of statis-tical science. Publishedpapers highlight the devel-opment of new and impor-tant sequentialapproaches, andinterdisciplinaryarticles empha-size the method-ology of practicalvalue to appliedresearchers andstatistical consultants.

Best Paper Award 2008from Journal ofNonparametric Statistics

The Journal of Nonparametric Statistics andthe ASA Section on NonparametricStatistics are pleased to give the BestPaper Award in 2008 to P.L. Davies andM. Meise for their paper Approximatingdata with weighted smoothing splinespublished in Journal of NonparametricStatistics Volume 20, Number 3, April2008, 207-228. This paper will be FREE toview online in 2009.

To access the paper and for further information about these and other journals, please visit: www.tandf.co.uk/journals.

Blurring the Line Between Users and DevelopersStatistical Genetics:Operating at the intersectionof human genetics and themathematical sciences

Keeping abreast of the ever-changing landscapeof human genetics, CRC resources continue toexplore the latest cutting-edge research that can make a difference in the lives of many.

story continued on pg. 6 (Statistical Genetics)

“One of the core features of the R environment, which it inherited from S, is thatthere is a deliberate blurring of the distinction between users and developers. Thesystem encourages and facilitates the development of new functionality. …newdevelopments in research are implemented in software and communicatedbetween researchers much more rapidly than ever before.”

—Paul Murrell

Statistical Genetics (continued from pg. 3)

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Interactive Graphics for DataAnalysisPrinciples and ExamplesMartin TheusMunich, GermanySimon UrbanekMadison, New Jersey, USACatalog no. C5947, January 2009, 290 pp. ISBN: 978-1-58488-594-8, $79.95 / £39.99

Logistic Regression ModelsJoseph M. HilbeJet Propulsion Laboratory, California Institute of Technology,Pasadena, USA and Arizona State University, Tempe, USACatalog no. C7575, May 2009, 656 pp. ISBN: 978-1-4200-7575-5, $79.95 / £48.99

Applied Statistical Inference withMINITABSally LesikCentral Connecticut State University, New Britain, USACatalog no. C6583, September 2009, c. 443 pp. ISBN: 978-1-4200-6583-1, $89.95 / £54.99

Regression ModelingMethods, Theory, and Computation withSASMichael PanikUniversity of Hartford, Connecticut, USACatalog no. C1972, April 2009, 830 pp. ISBN: 978-1-4200-9197-7, $99.95 / £60.99

ROC Curves for Continuous DataWojtek J. KrzanowskiUniversity of Exeter, UKDavid J. HandImperial College, London, UKCatalog no. K10031, May 2009, 232 pp. ISBN: 978-1-4398-0021-8, $69.95 / £42.99

Analysis of Messy DataVolume 1Designed Experiments, Second EditionGeorge A. Milliken and Dallas E. JohnsonKansas State University, Manhattan, USACatalog no. C3340, March 2009, 674 pp. ISBN: 978-1-58488-334-0, $89.95 / £54.99

Hidden Markov Models for TimeSeriesAn Introduction Using RWalter ZucchiniUniversity of Gottingen, GermanyIain L. MacDonaldUniversity of Cape Town, South AfricaCatalog no. C5734, April 2009, 288 pp. ISBN: 978-1-58488-573-3, $79.95 / £48.99

Introduction to SpatialEconometricsJames LeSageMcCoy College of Business Administration, San Marcos,Texas, USARobert Kelley PaceLouisiana State University, Baton Rouge, USACatalog no. C6424, January 2009, 340 pp. ISBN: 978-1-4200-6424-7, $89.95 / £54.99

A Handbook of StatisticalAnalyses using SASThird EditionGeoff DerUniversity of Glasgow, ScotlandBrian S. EverittKing's College, University of London, UKCatalog no. C7842, January 2009, 392 pp. Soft Cover, ISBN: 978-1-58488-784-3 $54.95 / £34.99

A Handbook of StatisticalAnalyses Using RSecond EditionBrian S. EverittKing's College, University of London, UKTorsten HothornCatalog no. C7933, August 2009, 376 pp. Soft Cover, ISBN: 978-1-4200-7933-3 $54.95 / £34.99

SAS and RData Management, Statistical Analysis,and GraphicsKen KleinmanHarvard Medical School, Boston, Massachusetts, USANicholas. J HortonSmith College, Northampton, Massachusetts, USACatalog no. C7057, July 2009, 351 pp. ISBN: 978-1-4200-7057-6, $69.95 / £44.99

Computational StatisticsAn Introduction to RGünther SawitzkiStatLab, Heidelberg, GermanyCatalog no. C6782, January 2009, 264 pp. ISBN: 978-1-4200-8678-2, $79.95 / £48.99

Introduction to ScientificProgramming and SimulationUsing ROwen Jones, Robert Maillardet, and Andrew RobinsonUniversity of Melbourne, Parkville, AustraliaCatalog no. C6872, March 2009, 472 pp. ISBN: 978-1-4200-6872-6, $79.95 / £48.99

Introduction to Data TechnologiesPaul MurrellThe University of Auckland, New ZealandCatalog no. C6517, February 2009, 418 pp. ISBN: 978-1-4200-6517-6, $69.95 / £42.99

Text MiningClassification, Clustering, andApplicationsEdited byAshok SrivastavaAmes Research Center, Moffett Field, California, USAMehran SahamiStanford University, California, USACatalog no. C5940, June 2009, 328 pp. ISBN: 978-1-4200-5940-3, $79.95 / £48.99

Statistical Detection andSurveillance of GeographicClustersPeter RogersonUniversity of Buffalo, New York, USAIkuho YamadaUniversity of Utah, Salt Lake City, USACatalog no. C9357, January 2009, 324 pp. ISBN: 978-1-58488-935-9, $89.95 / £57.99

Design and Analysis ofBioavailability and BioequivalenceStudiesThird EditionShein-Chung ChowDuke University School of Medicine, Durham, NorthCarolina, USAJen-pei LiuNational Taiwan University, TaipeiCatalog no. C6684, January 2009, 760 pp. ISBN: 978-1-58488-668-6, $99.95 / £63.99

Translational MedicineStrategies and Statistical MethodsEdited byDennis CosmatosWyethShein-Chung ChowDuke University School of Medicine, Durham, NorthCarolina, USACatalog no. C8725, January 2009, 224 pp. ISBN: 978-1-58488-872-7, $89.95 / £57.99

Data and Safety MonitoringCommittees in Clinical TrialsJay HersonJohn Hopkins University, Baltimore, Maryland, USACatalog no. C7037, March 2009, 191 pp. ISBN: 978-1-4200-7037-8, $89.95 / £54.99

Bayesian Methods for Measuresof AgreementLyle D. BroemelingMedical Lake, Washington, USACatalog no. C3414, January 2009, 340 pp. ISBN: 978-1-4200-8341-5, $89.95 / £54.99

Cluster Randomised TrialsRichard J. HayesLondon School of Hygiene & Tropical Medicine, UKLawrence H. MoultonJohns Hopkins Bloomberg School of Public Health,Baltimore, USACatalog no. C8164, January 2009, 338 pp. ISBN: 978-1-58488-816-1, $89.95 / £54.99

Design and Analysis of ClinicalTrials with Time-to-EventEndpointsEdited byKarl E. PeaceGeorgia Southern University, Statesboro, USACatalog no. C6639, April 2009, 616 pp. ISBN: 978-1-4200-6639-5, $99.95 / £60.99

Sample Sizes for Clinical TrialsSteven A. JuliousUniversity of Sheffield, UKCatalog no. C7397, August 2009, 323 pp. ISBN: 978-1-58488-739-3, $79.95 / £48.99

Meta-analysis and CombiningInformation in Genetics andGenomicsEdited byRudy GuerraRice University, Houston, Texas, USADarlene R. GoldsteinEPFL SB IMA STAT, SwitzerlandCatalog no. C522X, July 2009, 360 pp. ISBN: 978-1-58488-522-1, $99.95 / £63.99

Statistics in Human Genetics andMolecular BiologyCavan ReillyUniversity of Minnesota, Minneapolis, USACatalog no. C7263, June 2009, 280 pp. ISBN: 978-1-4200-7263-1, $59.95 / £38.99

Gene Expression Studies UsingAffymetrix MicroarraysHinrich Gohlmann and Willem TalloenJohnson & Johnson, Beerse, BelgiumCatalog no. C6515, June 2009, 359 pp. ISBN: 978-1-4200-6515-2, $89.95 / £54.99

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By now, most statisticians recognize R as one of themost flexible, integrated software environments forstatistical computing and graphics. Its exceptionalcapacity for adaptability and its sophisticated presen-tation features make it the first choice of experiencedstatisticians as well as researchers with minimal formaltraining in statistical software. Chapman & Hall/CRCPress offers an array of books to help readers at variouslevels understand R and make use of its wide range ofapplications.

Like its bestselling predecessor, A Handbook of Statistical Analyses Using R,Second Edition (August 2009) continues to offer straightforward descriptions of how touse R and interpret the results. Authors Brian S. Everitt and Torsten Hothorn includenew chapters on graphical displays, generalized additive models, and simultaneousinference, as well as a new section on generalized linear mixed models. In SAS and R:Data Management, Statistical Analysis, and Graphics (July 2009), Ken Kleinman andNicholas J. Horton present an easy way to learn how to perform an analytical task inboth SAS and R, without having to navigate through the extensive, idiosyncratic, andsometimes unwieldy software documentation. Introduction to Data Technologies(March 2009), written by R guru Paul Murrell, a member of the R Development CoreTeam, provides important information on how to work with research data. Murrelldevotes separate chapters to each computer language, including R.

Hidden Markov Models for Time Series: An Introduction Using R (April 2009),authored by Walter Zucchini and Iain L. MacDonald demonstrates how to applyHMMs to a wide range of time series types and discusses how to employ R to carry outcomputations for parameter estimation, model selection and checking, decoding, andforecasting. In Computational Statistics: An Introduction to R (January 2009),Günther Sawitzki also illustrates how R can be employed to tackle statistical problems.

Unlike most guides to R, Introduction to Scientific Programming and SimulationUsing R (March 2009) by Owen Jones, Robert Maillardet, and Andrew Robinson doesnot focus on the application of statistical techniques, but rather shows how to turnalgorithms into code. This introductory book surveys a range of useful mathematicaltools that either require or are illuminated by scientific computation. Another intro-ductory text, An Introduction to Statistical Inference and Its Applications with R(June 2009) by Michael W. Trosset, explains how statistical methods are used for dataanalysis and uses the elementary functions of R to perform the individual steps of sta-tistical procedures.

Most of these books offer code and supplemental material online. For more detailsabout any of these, or other related works, please visit www.crcpress.com.

The analysis of genetic data hasbecome a science unto itself, withstatisticians taking on a critical role.Three new titles from CRC Presscapture the state of the science inthis fascinating field.

Focusing on problems arising in theanalysis of genetics and genomics,Statistics in Human Genetics andMolecular Biology (July 2009) pro-vides a graduate textbook of statisti-cal methods used in genetics.Author Cavan Reilly covers clusteranalysis, combinatorial optimiza-tion, and dynamic programming,along with the core topics of genomemapping, biological sequence analy-sis, and the analysis of gene expres-sion arrays. He also exploresBayesian approaches, such as hiddenMarkov models and black motifmethods, along with modern tools ofBayesian analysis, including Markovchain Monte Carlo (MCMC).

With the diversity of data and meta-data now available, there isincreased interest in analyzing mul-tiple studies beyond statisticalapproaches of formal meta-analysis.Meta-analysis and CombiningInformation in Genetics andGenomics (July 2009) is the firstbook devoted to meta-analysis ingenetics. Addressing the complica-tions inherent in comparing andreplicating genetic studies, leadingexperts consider microarrays, genemapping, and proteomics whiledetailing the various techniquesused to analyze and combine datafrom different types of studies.

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66 33

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One of the best tools forgene expression studies is theAffymetrix GeneChip®. Thisplatform is the most broadlyadopted single-color tech-nology used in academic labs, clinics, and commercial entities. GeneExpression Studies Using Affymetrix Microarrays (CRC Press, June 2009) pro-vides a detailed and systematic introduction as well as a practical user’s guide tothis promising technology, including the use of R and other software formicroarray analysis. The text also includes a wealth of information and key ter-minology regarding microarray gene expression studies in general.

Communications in Statistics: Simulationand Computation and Communications inStatistics: Theory and Methods includespapers on advance theory and methods inthe computational aspects of Probabilityand Statistics. This pair of related journals,edited by N. Balakrishnan, presents formu-lation and discussion of problems as well assolutions in all areas of statistics, includinggraphic methods, resampling, and compu-tationally intensive methods. In addition,the journals provide exhaustive coverageand up-to-date reviews of key topics bypublishing special issues.

Sequential Analysis, edited by NitisMukhopadhyay, contributes to theoreticaland applied aspects of sequential andapplied methodologies in all areas of statis-tical science. Publishedpapers highlight the devel-opment of new and impor-tant sequentialapproaches, andinterdisciplinaryarticles empha-size the method-ology of practicalvalue to appliedresearchers andstatistical consultants.

Best Paper Award 2008from Journal ofNonparametric Statistics

The Journal of Nonparametric Statistics andthe ASA Section on NonparametricStatistics are pleased to give the BestPaper Award in 2008 to P.L. Davies andM. Meise for their paper Approximatingdata with weighted smoothing splinespublished in Journal of NonparametricStatistics Volume 20, Number 3, April2008, 207-228. This paper will be FREE toview online in 2009.

To access the paper and for further information about these and other journals, please visit: www.tandf.co.uk/journals.

Blurring the Line Between Users and DevelopersStatistical Genetics:Operating at the intersectionof human genetics and themathematical sciences

Keeping abreast of the ever-changing landscapeof human genetics, CRC resources continue toexplore the latest cutting-edge research that can make a difference in the lives of many.

story continued on pg. 6 (Statistical Genetics)

“One of the core features of the R environment, which it inherited from S, is thatthere is a deliberate blurring of the distinction between users and developers. Thesystem encourages and facilitates the development of new functionality. …newdevelopments in research are implemented in software and communicatedbetween researchers much more rapidly than ever before.”

—Paul Murrell

Statistical Genetics (continued from pg. 3)

Sample Size Calculations in ClinicalResearch, Second Edition & N Solution 2007 BasicThe Book: This second edition of a bestselling refer-ence features new chapters on dose finding, microarraystudies, and Bayesian approaches. The Softwareenables quick and easy sample size calculation andpower analysis. It contains all the procedures for testingequality, superiority/non-inferiority and equivalence forcomparing means, proportions, and time-to-event

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David J. Hand is a professor of statisticsat Imperial College in London, whereamong his diverse areas of responsibility,he is chair of Statistical Methods in theRetail Financial Services ResearchGroup. Dr. Hand serves as president ofthe Royal Statistical Society and is aFellow of the British Academy. He has

been a recipient of the Guy Medal of the Royal StatisticalSociety, the Royal Society Wolfson Research MeritAward, and the IEEE ICDM Research ContributionsAward. He has published countless papers and more than20 books, including the newly available ROC Curvesfor Continuous Data (May 2009), with co-author Wojtek J. Krzanowski. He alsolaunched the journal Statistics andComputing and served a term as editor ofthe Journal of the Royal Statistical Society,Series C. Dr. Hand’s broad researchinterests include multivariate statis-tics, classification methods, patterndetection, and the interface betweenstatistics and computing. His influenceand advice has led to improvements inorganizations ranging from banks to phar-maceutical companies to governments.

A key member of the development team forthe R and Omegahat projects, Paul Murrell isone of the foremost innovators of the R envi-ronment. Focusing his research on statisticalcomputing and graphics, Dr. Murrell is theauthor of the bestselling R Graphics (2005)and the recently published Introduction toData Technologies (February 2009). He is asenior lecturer at the University of Auckland. It was atAuckland that he came under the influence of RobertGentleman and Ross Ihaka—the noted R & R team that orig-inated the R environment—and found his path. “When I

found myself writing my own plotting software to producecharts for a psychology project report, I took it as an

indicator of where my interests really lay.”Currently, XML and its vast research potential

has Dr. Murrell’s attention. As for the futureof statistics, he says, “From a research per-spective, developing software tools needs tobe a recognized career path in statistics sothat people consider it as an option and arerewarded for making that career choice.From an educational perspective, statistics

curriculums need to formally address topicsin statistical computing, so that people have

the necessary skills to be able to engage in soft-ware research.” Those lucky enough to assist him

or attend his lectures have ample opportunity to wit-ness both those perspectives in practice.

2

StatisticsNewS What’s News StatisticsNewS PioneersPlotting the Future

SATURDAY, AUGUST 1

Missing Data in Longitudinal Studies: Strategies for BayesianModeling and Sensitivity Analysis- Continuing Education Course CE_02C! Michael J. Daniels and Joseph W. Hogan authors of Missing Data

in Longitudinal Studies: Strategies for Bayesian Modeling andSensitivity Analysis teach this course.

SUNDAY, AUGUST 2

Tolerance Intervals: Theory, Applications, and Computation- Continuing Education Course CE_10C! Presented by Kalimuthu Krishnamoorthy, author of Handbook of

Statistical Distributions with Applications with Thomas Mathew. Hierarchical Modeling and Analysis of Spatial-Temporal Data:Emphasis in Forestry, Ecology, and Environmental Sciences- Continuing Education Course CE_12C! Sudipto Banerjee and Andrew Finley give a short course based on

Banerjee’s book: Hierarchical Modeling and Analysis for SpatialData.

Recent Advances in Bayesian Model Selection Invited Papers 3! David Madigan, editor of the Computer Science and Data Analysis

series, discusses Sequential Bayesian Model Selection.Introductory Overview Lecture: Largely About Largeness:Models And Views For High-Dimensional Data 41! Organized by Wendy Martinez, co-author of the forthcoming

Exploratory Data Analysis with MATLAB, Second Edition.The Issue of High Dimensionality and Missing Data in ComplexEpidemiological Studies Invited Papers 46! Nicholas P. Jewell, author of Statistics for Epidemiology, presents

Estimating and Ranking the Impact of High-Dimensional EnvironmentalRisk Factors In Environmental Epidemiology.

MONDAY, AUGUST 3

Dose Finding Studies: Methods and Implementation- Continuing Education Course CE_16C! Frank Bretz, co-author of Multiple Comparisons Using R teaches

this course with Jose C. Pinheiro.IMS Medallion Lecture II Invited Papers 96! Chaired by Marina Meila, co-editor of the Computer Science and

Data Analysis series.Recent Advances in Missing Data and Causal Inference InvitedPapers 131! Geert Molenberghs, co-author of Longitudinal Data Analysis dis-

cusses Every Missing Not At Ranom Model for Incomplete Data HasGot a Missing At Random Counterpart With Equal Fit. Joseph L.Schafer, author of Analysis of Incomplete Multivariate Data,offers Causal Modeling When the Treatment is a Latent Class.

JASA, Theory and Methods Invited Session Invited Papers 140! Raymond J Carroll, co-author of Measurement Error in

Nonlinear Models: A Modern Perspective, Second Editionpresents Prediction in Measurement Error Models with AuroreDelaigle. Jianqing Fan, co-editor of the Monographs on Statistics and Applied Probability series and Susan Schennah are discussants inthis session.

Recent Advances and the Future of Statistics Invited Papers198! Brad Efron, co-author of An Introduction to the Bootstrap, exam-

ines The Future of Indirect Evidence. TUESDAY, AUGUST 4

Monte Carlo and Bayesian Computation with R - Continuing Education Course CE_25C! Maria Rizzo, author of Statistical Computing with R, instructs this

course with James Albert.Comprehensive Data Analysis Using Interactive StatisticalGraphics - Continuing Education Course CE_26C! Martin Theus and Simon Urbanek present a short course based on

their book Interactive Graphics for Data Analysis: InteractiveGraphics for Data Analysis.

Advances in Spatial and Spatio-Temporal Modeling InvitedPapers 246! Christopher Wikle, co-editor of the Interdisciplinary Statistics series,

presents A Class of Nonlinear Spatio-Temporal Dynamic Models.IMS Medallion Lecture IV Invited Papers 250! Chaired by Galin L. Jones, co-author of Handbook of Markov

Chain Monte Carlo: Methods and Applications.Emerging Trends and Evidence in High-Dimensional "Omics"investigations Invited Papers 290! David B. Allison, co-editor of DNA Microarrays and Related

Genomics Techniques: Design, Analysis, and Interpretation ofExperiments, explores Biostatistics' Rise As an Empirical Science:Genomic Research as the Leavening Agent.

Random Effect Specification/Misspecification in Spatial/Spatio-temporal Health Modeling Invited Papers 291! Organized by Andrew B. Lawson, author of Bayesian Disease

Mapping: Hierarchical Modeling in Spatial Epidemiology.WEDNESDAY, AUGUST 5

Bayesian Methods in Medicine and Biology Invited Papers 448! Organized by Wesley O. Johnson, co-author of Bayesian Ideas and

Data Analysis: An Introduction for Scientists and Statisticians.Statistics in Defense and National Security Roundtable withLunch 487! Organized by Jeffrey L. Solka, co-author of Exploratory Data

Analysis with MATLAB, Second Edition.THURSDAY, AUGUST 6

Introductory Overview Lecture: Statistical Learning and DataMining 535! David J. Hand, co-author of ROC Curves for Continuous Data,

explores Statistical Learning and Data Mining.The Influence of Psychology, Cartography and ComputerScience on the Design of Interactive Graphics for SpatialStatistical Data Invited Papers 540! Organized by Linda Williams Pickle, co-author of Visualizing Data

Patterns with Micromaps with Daniel B. Carr. Carr presentsVisualizing Patterns in Data with Micromaps in this session.

2 77

! ! ! JSM HIGHLIGHTS ! ! !

There are also several invited session categories that are cho-sen at the discretion of the JSM Program Chair, such as ASAjournals, outside societies, ASA committees, memorial ses-sions, and Introductory Overview Lectures. The GeneralMethods Chairs and I also had someinvited session slots that we could fill.Thus, we could somewhat shape theprogram via these selections.

Most of the invited program was final-ized by December 2008, after which weturned our attention to the contributedprogram. Proposed invited sessions that did not make it on theprogram could be converted to topic contributed sessions.However, for the most part, the JSM Program Committee justhad to wait for contributed abstracts to roll in, which they did.We received approximately 500 more abstracts (on average)than the previous two meetings! Needless to say, this present-ed some significant scheduling challenges to the ASAMeetings Department and the JSM Program Committee, andeveryone did a great job handling these and many other issues.

You probably realize by now that I had a lot of help puttingthe program together, and I would like to take this opportu-nity to acknowledge the help of many people. First, I wouldlike to thank our ASA President, Sally Morton for her excel-

lent leadership and support. The JSMProgram Committee, the GeneralMethods Chairs, and the Poster Chairdid the bulk of the work organizing theprogram. In particular, our JSM PosterChair, Lara Schmidt did an outstand-ing job putting together and promoting

poster sessions. We could not do anything without the won-derful people at the ASA Meetings Department: DonnaArrington, Kathleen Wert, and Steve Porzio. I want to say aspecial thank you to Donna, who was responsible for the JSMtechnical program. She was always helpful, cheerful, andpatient with our many questions and issues. Of course, theJSM could not happen without the participation of the pre-senters, chairs, organizers, exhibitors, and all attendees – sothank you for making this JSM an event to remember!

JSM 2009 (continued from pg. 1)

I think that this is one of the important challenges that all members of the programcommittee faced – organizing a balancedprogram and not letting our biases and

interests motivate our choices.

Joint Statistical Meetings 2009

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It was several weeks before JSM 2007 when I got the email– “Was I interested in being the JSM 2009 ProgramChair?” I immediately replied with a resounding YES! Myname and others were then submitted as potential chairs tothe Committee on Meetings. The committee chose me as the2009 Program Chair, and my election was subsequentlyapproved by Sally Morton, our current ASA President, andthe ASA Executive Board.

One of my first tasks in late 2007 was to appoint two GeneralMethods Chairs and the Poster Chair. These folks are veryimportant members of the program committee because theyhelp me in many ways, such as with organizing the program,filling in for other committee members, and more. The remaining members of theprogram committee are either appointed by a member society (e.g., IMS, SSC,etc.) or elected by their section.

Around this same time, ASA President Morton picked the theme for our meeting,and she chose an exciting one – Statistics: From Evidence to Policy. While sessionsdo not have to address it, the theme is very important, as it tends to drive the con-tent of the invited and topic-contributed sessions.

To gain experience in the process, the JSM 2009 Program Committee had toarrange some events for the previous year’s program. For example, the committeewas responsible for organizing and scheduling the roundtable coffees and lunchesfor the 2008 meetings. We also started working on the invited program during thissame time period – one year before our JSM!

Then, the hard (but exciting and fun!) work begins. The partner soci-eties, the ASA sections, and other entities have a certain number of

invited sessions allocated to them. The appropriate committeemembers can organize the sessions or solicit ideas from their

members. The representatives for the ASA sections, com-mittees, and interest groups are allowed to submit two

invited sessions for the competition slots, and theentire program committee votes on the ones they

think should be included in the program. Thiswas certainly a challenging and important part

of the process because the invited sessionsare the backbone of the meetings.

Fit linearmodels without

intercepts in SAS

or R

Making the Most Out of those Complex Datasetsin the Social and Behavioral Sciences

StatisticsNewS Nexus

Summer 2009

JSM 2009 – A Behind the Scenes Look

SASproc glm data=ds;

model y = x1 ... xk / noint;

run;

Note: The noint option works with many modelstatements.

Rmod1 < –lm(y ~ 0 + x1 + ... + xk,data=ds)

or

mod1 < –lm(y ~ x1 + ... + xk –1,data=ds)

Large and complex datasets are becoming increasingly prevalent in the social andbehavioral sciences. The Chapman & Hall/CRC Statistics in the Social and BehavioralSciences Series makes the lives of statisticians working in the applied sciences easier byexploring the effective and efficient use of statistical, econometric, and psychometricmethods. Two new books in the series explore the latest methods and tools in thisrapidly growing area.

Emphasizing causation as a functional relationship between variables that describeobjects, Linear Causal Modeling with Structural Equations (2009), by Stanley A.Mulaik, integrates a general philosophical theory of causation with structural equationmodeling that concerns the special case of linear causal relations. In addition todescribing how the functional relation concept may be generalized to treat probabilis-tic causation, the book reviews historical treatments of causation and explores recentdevelopments in experimental psychology on studies of the perception of causation. InMultivariable Modeling and Multivariate Analysis for the Behavioral Sciences(2009)Brian S. Everitt goes beyond the usual coverage of hypothesis testing to provide a fullexplanation of the most useful methods for comparing and examining data in psychol-ogy, the social sciences, and law. He includes real data sets that provide a sampling ofthe issues commonly encountered when applying statistics in practice.

In partnership with Stata Press, Chapman Hall/CRC offers Microeconometrics UsingStata (2009), co-authored by A. Colin Cameron and Pravin K. Trivedi, provides an out-standing introduction to microeconometrics and microeconometric research usingStata. Cameron and Trivedi cover specialized topics often left out of microeconomet-rics texts and basic introductions to Stata. They address each topic with an in-depthStata example and demonstrate how to use Stata’s programming features to implementmethods for which Stata does not have a specific command.

Although interest in spatial regression models has surgedin recent years, a comprehensive, up-to-date text onthese approaches does not exist. Rounding outthe collection, Introduction to SpatialEconometrics (January 2009), presents a vari-ety of regression methods used to analyzespatial data samples that violate the tradi-tional assumption of independencebetween observations. James LeSage andR. Kelley Pace explore recent advancesin spatial econometric models—includ-ing some previously unpublishedresults.

A. Colin Cameron joins the distinguished panel of series editors that include Andrew Gelman,J. Scott Long, Sophia Rabe-Hesketh, and Anders Skrondal. Dr. Cameron is a professor in the

Department of Economics at the University of California, Davis and has served as theDirector of the U.C. Davis Center on Quantitative Social Science Research.

Visit Chapman & Hall/CRCat Booth 304Every year at JSM Chapman & Hall/CRC offers attendees the chance to saveon the best statistics books available.This year is no exception. You can buildyour library without breaking your bank.Save 50% on these classic books:

Extending the Linear Model with R:Generalized Linear, Mixed Effects andNonparametric Regression Models, Julian J. Faraway

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R Graphics, Paul Murrell

Handbook of Parametric andNonparametric Statistical Procedures,David J. Sheskin

Linear Mixed Models: A PracticalGuide Using Statistical Software,Brady West, Kathleen B. Welch, andAndrzej T. Galecki

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Routines for linear models in SAS andR incorporate an intercept by default.But, what's the syntax for fitting amodel without an intercept? This entryfrom SAS and R: Data Management,Statistical Analysis, and Graphics(July 2009) will tell you.

The book is designed as a bilingualSAS/R dictionary: If you know SAS,you can look up the noint option in theSAS index. If you know R, you canlook up the 0 operator, or if you'reunaware of how to do it in either, youcan look up no intercept in the subjectindex.

story continued on pg. 7 (JSM 2009)

JSM 2009 - Washington, DCFrom Evidence to Policy

Wendy L. MartinezJSM 2009

Program Chair