Transcript
Page 1: Nonparametric estimation of - Department of …economics.yale.edu/sites/default/files/wellner_metrics...Nonparametric estimation of s concave and log-concave densities: alternatives

Nonparametric estimation of

s−concave and log-concave densities:

alternatives to maximum likelihood

Jon A. Wellner

University of Washington, Seattle

Cowles Foundation Seminar, Yale University

November 9, 2016

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Cowles Foundation Seminar, Yale University

Based on joint work with:

• Qiyang (Roy) Han

• Charles Doss

• Fadoua Balabdaoui

• Kaspar Rufibach

• Arseni Seregin

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Outline

• A: Log-concave and s−concave densities on R and Rd

• B: s−concave densities on R and Rd

• C: Maximum Likelihood for log-concave and s−concave

densities

B 1: Basics

B 2: On the model

B 3: Off the model

• D. An alternative to ML: Renyi divergence estimators

B 1. Basics

B 2. On the model

B 3. Off the model

• E. Summary: problems and open questions

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A. Log-concave densities on R and Rd

If a density f on Rd is of the form

f(x) ≡ fϕ(x) = exp(ϕ(x)) = exp (−(−ϕ(x)))

where ϕ is concave (so −ϕ is convex), then f is log-concave.The class of all densities f on Rd of this form is called the classof log-concave densities, Plog−concave ≡ P0.

Properties of log-concave densities:

• Every log-concave density f is unimodal (quasi concave).

• P0 is closed under convolution.

• P0 is closed under marginalization.

• P0 is closed under weak limits.

• A density f on R is log-concave if and only if its convolutionwith any unimodal density is again unimodal (Ibragimov,1956).

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• Many parametric families are log-concave, for example:

B Normal (µ, σ2)

B Uniform(a, b)

B Gamma(r, λ) for r ≥ 1

B Beta(a, b) for a, b ≥ 1

• tr densities with r > 0 are not log-concave.

• Tails of log-concave densities are necessarily sub-exponential.

• Plog−concave = the class of “Polya frequency functions of

order 2”, PF2, in the terminology of Schoenberg (1951) and

Karlin (1968). See Marshall and Olkin (1979), chapter 18,

and Dharmadhikari and Joag-Dev (1988), page 150. for nice

introductions.

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B. s− concave densities on R and Rd

If a density f on Rd is of the form

f(x) ≡ fϕ(x) =

(ϕ(x))1/s, ϕ convex, if s < 0exp(−ϕ(x)), ϕ convex, if s = 0(ϕ(x))1/s, ϕ concave, if s > 0,

then f is s-concave.

The classes of all densities f on Rd of these forms are called

the classes of s−concave densities, Ps. The following inclusions

hold: if −∞ < s < 0 < r <∞, then

Pr ⊂ P0 ⊂ Ps ⊂ P−∞

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Properties of s-concave densities:

• Every s−concave density f is quasi-concave.

• The Student tν density, tν ∈ Ps for s ≤ −1/(1 + ν). Thus theCauchy density (= t1) is in P−1/2 ⊂ Ps for s ≤ −1/2.

• The classes Ps have interesting closure properties underconvolution and marginalization which follow from theBorell-Brascamp-Lieb inequality: let 0 < λ < 1, −1/d ≤ s ≤∞, and let f, g, h : Rd → [0,∞) be integrable functions suchthat

h(λx+ (1− λ)y) ≥Ms(f(x), g(y);λ) for all x, y ∈ Rd

where

Ms(a, b;λ) = (λas + (1− λ)bs)1/s, M0(a, b;λ) = aλb1−λ.

Then∫Rdh(x)dx ≥Ms/(sd+1)

(∫Rdf(x)dx,

∫Rdg(x)dx;λ

).

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Special Case: s = 0: Prekopa-Leindler inequality.

Let 0 < λ < 1 and let f, g, h : Rd → [0,∞). if

h(λx+ λy) ≥ f(x)λg(y)1−λ for all x, y ∈ Rd,

then ∫Rdh(x)dx ≥

(∫Rdf(x)dx

)λ (∫Rdg(x)dx

)1−λ.

Proposition. (a) Marginalization preserves log-concavity.

(b) Convolution preserves log-concavity.

Proof. (a) Suppose that h(x, y) is a log-concave density on

Rm × Rn. Thus

h(λ(x, y) + (1− λ)(x′, y′)) ≥ h(x, y)λh(x′, y′)1−λ.

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We want to show that

f(y) ≡∫Rm

h(x, y)dx is log-concave.

With h(x) ≡ h(x, λy + λy′), f(x) ≡ h(x, y), g(x) ≡ h(x, y′),

h(λx+ λx′) = h(λx+ λx′, λy + λy′)

≥ h(x, y)λh(x′, y′)λ = f(x)λg(x′)λ.

Thus Prekopa-Leindler yields

f(λy + (1− λ)y′) ≥(∫

Rmh(x, y)dx

)λ (∫Rm

h(x, y′)dx)1−λ

= f(y)λf(y′)1−λ.

(b) If X ∼ f and Y ∼ g are independent, f, g log-concave, then

(X,Y ) ∼ f ·g ≡ h is log-concave. Since log-concavity is preserved

under affine transforms (X−Y,X+Y ) has a log-concave density.

By (a), X + Y is log-concave. �

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• If f ∈ Ps and s > −1/(d+ 1), then Ef‖X‖ <∞.

• If f ∈ Ps and s > −1/d, then ‖f‖∞ <∞.

• If f ∈ P0, then for some a > 0 and b ∈ R

f(x) ≤ exp(−a‖x‖+ b) for all x ∈ R.

• If f ∈ Ps and s > −1/d, then with r ≡ −1/s > d, then for

some a, b > 0

f(x) ≤ (b+ a‖x‖)−r for all x ∈ R.

• If s < −1/d then there exists f ∈ Ps with ‖f‖∞ =∞.

• If −1/d < s ≤ −1/(d + 1), then there exists f ∈ Ps with

Ef‖X‖ =∞.

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-4 -2 2 4

0.02

0.04

0.06

0.08

0.10

f(x) =1

√rBeta(r/2,1/2)

(r

r + x2

)(1+r)/2,

with r = .05, so f ∈ P−1/(1+.05)

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-4 -2 2 4

0.05

0.10

0.15

0.20

0.25

0.30

tr densities, r ∈ {0.1,0.2, . . . ,1.0}

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-3 -2 -1 0 1 2 3

1

2

3

4

f(x) =1

2Γ(1/2)|x|−1/2exp(−|x|), with f ∈ P−2.

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C. Maximum Likelihood:

0-concave and s-concave densities

MLE of f and ϕ: Let C denote the class of all concave function

ϕ : R→ [−∞,∞). The estimator ϕn based on X1, . . . , Xn i.i.d. as

f0 is the maximizer of the “adjusted criterion function”

`n(ϕ) =∫

logfϕ(x)dFn(x)−∫fϕ(x)dx

=

∫ϕ(x)dFn(x)−

∫eϕ(x)dx, s = 0,∫

(1/s)log(−ϕ(x))+dFn(x)−∫

(−ϕ(x))1/s+ dx, s < 0,

over ϕ ∈ C.

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1. Basics

• The MLE’s for P0 exist and are unique when n ≥ d+ 1.

• The MLE’s for Ps exist for s ∈ (−1/d,0) when

n ≥ d(

r

r − d

)where r = −1/s. Thus n→∞ as −1/s = r ↘ d.

• Uniqueness of MLE’s for Ps?

• MLE ϕn is piecewise affine for −1/d < s ≤ 0.

• The MLE for Ps does not exist if s < −1/d. (Well known for

s = −∞ and d = 1.)

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2. On the model

• The MLE’s are Hellinger and L1− consistent.

• The log-concave MLE’s fn,0 satisfy∫ea|x||fn,0(x)− f0(x)|dx→a.s. 0.

for a < a0 where f0(x) ≤ exp(−a0|x|+ b0).

• The s−concave MLE’s are computationally awkward; log is“too aggressive” a transform for an s−concave density. [Notethat ML has difficulties even for location t− families: multipleroots of the likelihood equations.]

• Pointwise distribution theory for fn,0 when d = 1;no pointwise distribution theory for fn,s when d = 1;no pointwise distribution theory for fn,0 or fn,s when d > 1.

• Global rates? H(fn,s, f0) = Op(n−2/5) for −1 < s ≤ 0, d = 1.

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3. Off the modelNow suppose that Q is an arbitrary probability measure on Rdwith density q and X1, . . . , Xn are i.i.d. q.

• The MLE fn for P0 satisfies:∫Rd|fn(x)− f∗(x)|dx→a.s. 0

where, for the Kullback-Leibler divergence

K(q, f) =∫qlog(q/f)dλ,

f∗ = argminf∈P0(Rd)K(q, f)

is the “pseudo-true” density in P0(Rd) corresponding to q.In fact: ∫

Rdea‖x‖|fn(x)− f∗(x)|dx→a.s. 0

for any a < a0 where f∗(x) ≤ exp(−a0‖x‖+ b0).

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• The MLE fn for Ps does not behave well off the model.

Retracing the basic arguments of Cule and Samworth (2010)

leads to negative conclusions. (How negative remains to be

pinned down!)

Conclusion: Investigate alternative methods for estimation in

the larger classes Ps with s < 0! This leads to the proposals by

Koenker and Mizera (2010).

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D. An alternative to ML:

Renyi divergence estimators

0. Notation and Definitions

• β = 1 + 1/s < 0,

α−1 + β−1 = 1.

• C(X) = all continuous functions on conv(X).

• C∗(X) = all signed Radon measures on C(X) = dual space of C(X).

• G(X) = all closed convex (lower s.c.) functions on conv(X).

• G(X)◦ = {G ∈ C∗(X) :∫gdG ≤ 0 for all g ∈ G(X}, the polar

(or dual) cone of G(X).

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Primal problems: P0 and Ps:

• P0: ming∈G(X)L0(g,Pn) where

L0(g,Pn) = Png +∫Rd

exp(−g(x))dx.

• Ps: ming∈G(X)Ls(g,Pn) where

Ls(g,Pn) = Png +1

|β|

∫Rdg(x)βdx.

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Dual problems: P0 and Ps:

• D0: maxf {−∫f(y)logf(y)dy} subject to

f(y) =d(Pn −G)

dyfor some G ∈ G(X)◦.

• Ds: maxf∫ f(y)α

α dy subject to

f(y) =d(Pn −G)

dyfor some G ∈ G(X)◦.

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Why do these make sense?

• Population version of P0: ming∈G L0(g, f0) where

L0(g, f0) =∫{g(x)f0(x) + e−g(x)}dx.

Minimizing the integrand pointwise in g = g(x) for fixed f0(x)

yields f0(x)− e−g(x) = 0 if e−g(x) = f0(x).

• Population version of Ps: ming∈G Ls(g, f0) where

Ls(g, f0) =∫{g(x)f0(x) +

1

|β|gβ(x)}dx.

Minimizing the integrand pointwise in g = g(x) for fixed f0(x)

yields f0(x)+(β/|β|)gβ−1(x) = f0(x)−gβ−1(x) = 0, and hence

g1/s = g1/s(x) = f0(x).

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1. Basics for the Renyi divergence estimators:

• (Koenker and Mizera, 2010) If conv(X) has non-empty

interior, then strong duality between Ps and Ds holds. The

dual optimal optimal solution exists, is unique, and fn = g1/sn .

• (Koenker and Mizera, 2010) The solution f = g1/s in the

population version of the problem when Q = P0 has density

p0 ∈ Ps is Fisher-consistent; i.e. f = p0.

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2. Off the model: Han & W (2015)

Let

Q1 ≡ {Q on (Rd,Bd) :∫‖x‖dQ(x) <∞},

Q0 ≡ {Q on (Rd,Bd) : int(csupp(Q)) 6= ∅}.

• Theorem (Han & W, 2015): If −1/(d + 1) < s < 0 and

Q ∈ Q0 ∩ Q1, then the primal problem Ps(Q) has a unique

solution g ∈ G which satisfies f = g1/s where g is bounded

away from 0 and f is a bounded density.

• Theorem (Han & W, 2015): Let d = 1. If fn,s denotes

the solution to the primal problem Ps and fn,0 denotes the

solution to the primal problem P0, then for any κ > 0, p ≥ 1,∫(1 + |x|)κ|fn,s(x)− fn,0(x)|pdx→ 0 as s↗ 0.

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• Theorem (Han & W, 2015): Suppose that:

(i) d ≥ 1,

(ii) −1/(d+ 1) < s < 0, and

(iii) Q ∈ Q0 ∩Q1.

If fQ,s denotes the (pseudo-true) solution to the primal

problem Ps(Q), then for any κ < r − d = (−1/s)− d,∫(1 + |x|)κ|fn,s(x)− fQ,s(x)|dx→a.s. 0 as n→∞.

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3. On the model: Q has density f ∈ Ps′; f = g1/s′ for some g

convex.

• Consistency: Suppose that: (i) d ≥ 1 and −1/d < s < 0 and

s′ > s if s ≤ −1/(d + 1), s′ = s if s > −1/(d + 1). Then for

any κ < r − d = (−1/s)− d,∫(1 + |x|)κ|fn,s(x)− f(x)|dx→a.s. 0 as n→∞.

Thus H(fn,s, f)→a.s. 0 as well.

• Pointwise limit theory: (paralleling the results of Balabdaoui,

Rufibach, and W (2009) for s = 0)

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Assumptions:

• (A1) g0 ∈ G and f0 ∈ Ps(R) with −1/2 < s < 0.

• (A2) f0(x0) > 0.

• (A3) g0 is locally C2 in a neighborhood of x0 with

g0′′(x0) > 0.

Theorem 1. (Pointwise limit theorem; Han & W (2016))

Under assumptions (A1)-(A3), we have

n25(gn(x0)− g0(x0)

)n

15(g′n(x0)− g′0(x0)

)→d

−(g4

0(x0)g(2)0 (x0)

r4f0(x0)2(4)!

)1/5

H(2)2 (0)

−(g2

0(x0)[g

(2)0 (x0)

]3r2f0(x0)3

[(4)!

]3)1/5

H(3)2 (0)

,

and . . .

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. . . furthermore

n25(fn(x0)− f0(x0)

)n

15(f ′n(x0)− f ′0(x0)

)→d

(rf0(x0)3g

(2)0 (x0)

g0(x0)(4)!

)1/5

H(2)2 (0)(

r3f0(x0)4(g

(2)0 (x0)

)3

g0(x0)3[(4)!

]3)1/5

H(3)2 (0)

,

where H2 is the unique lower envelope of the process Y2 satisfying

1. H2(t) ≤ Y2(t) for all t ∈ R;

2. H(2)k is concave;

3. H2(t) = Y2(t) if the slope of H(2)2 decreases strictly at t.

4. Y2(t) =∫ t0W (s)ds− t4, t ∈ R where W is two-sided Brownian

motion started at 0.

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• Estimation of the mode for d = 1.

Theorem 2. (Estimation of the mode) Assume (A1)-(A4)

hold. Then

n1/5(mn −m0

)→d

(g0(m0)2(4)!2

r2f0(m0)g(2)0 (m0)2

)1/5

M(H(2)2 ), (1)

where mn = M(fn),m0 = M(f0).

• What is the price of assuming s < 0 when the truth f ∈ P0?

Assume −1/2 < s < 0 and k = 2. Let f0 = exp(ϕ0) be a log-

concave density where ϕ0 : R → R is the underlying concave

function. Then f0 is also s-concave.

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Let gs := f−1/r0 = exp(−ϕ0/r) be the underlying convex function

when f0 is viewed as an s-concave density. Calculation yields

g(2)s (x0) =

1

r2gs(x0)

(ϕ′0(x0)2 − rϕ′′0(x0)

).

Hence the constant before H(2)2 (0) appearing in the limit

distribution for fn becomes(f0(x0)3ϕ′0(x0)2

4!r+f0(x0)3|ϕ′′0(x0)|

4!

)1/5

.

The second term is the constant involved in the limiting

distribution when f0(x0) is estimated via the log-concave MLE:

(2.2), page 1305 in Balabdaoui, Rufibach, & W (2009). The

ratio of the two constants (or asymptotic relative efficiency) is

shown for f0 standard normal (blue) and logistic (magenta) in

the figure:

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-4 -2 0 2 4

0.2

0.4

0.6

0.8

1.0

Efficiency ratio: f0 = N(0,1), blue; f0 =logistic, magenta

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• The first term is non-negative and is the price we pay by es-

timating a true log-concave density via the Renyi divergence

estimator over a larger class of s-concave densities.

• Note that the first term vanishes as r →∞ (or s↗ 0).

• Note that the ratio is 1 at the mode of f0.

• For estimation of the mode, the ratio of constants is always

1: nothing is lost by enlarging the class from s = 0 to s < 0!

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E. Summary: problems and open questions

• Global rates of convergence?

• Limiting distribution(s) for d > 1? (nr with r = 2/(4 + d)?)

• MLE (rate-) inefficient for d ≥ 4 (or perhaps d ≥ 3)? How to

penalize to get efficient rates?

• Can we go below s = −1/(d+ 1) with other methods?

• Multivariate classes with nice preservation/closure properties

and smoother than log-concave?

• Algorithms for computing fn ∈ Ps? (Non-smooth and

convex; or non-smooth and non-convex?)

• Related results for convex regression on Rd: Seijo and Sen,

Ann. Statist. (2011).

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Guvenen et al (2014)

have estimated models of income dynamics using very large (10percent) samples of U.S. Social Security records linked to W2data The density is not log-concave, but an s−concave densitywith s = −1/2 fits well:

−4 −2 0 2 4

−8

−6

−4

−2

0

x ~ log income annual increments

log

f(x)

−4 −2 0 2 4

−12

0−

80−

60−

40−

200

x ~ log income annual increments

−1

f(x)

−2 0 2

01

23

x ~ log income annual increments

f(x)

Courtesy Roger Koenker

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F. Selected references

• Dumbgen and Rufibach (2009).

• Cule, Samworth, and Stewart (2010)

• Cule and Samworth (2010).

• Dumbgen, Samworth, and Schuhmacher (2011).

• Balabdaoui, Rufibach, and W (2009)

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Many thanks!

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