ICSA Applied Statistics Symposium 1. Balanced adjusted empirical likelihood

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1 ICSA Applied Statistics Symposium 1 Balanced adjusted empirical likelihood Art B. Owen Stanford University Sarah Emerson Oregon State University

2 ICSA Applied Statistics Symposium 2 Empirical likelihood Observations x i R d, x i iid F0 L(F ) = n F {X i } i=1 Nonparametric MLE is at F = F n = 1 n n i=1 δ x i ECDF Kiefer & Wolfowitz (1956) Some other NPMLE results: Survival analysis Kaplan & Meier (1958) Survey sampling Hartley & Rao (1967) Interval censoring Peto (1973) Censoring & truncation Lynden-Bell (1971) Monotone density Grenander (1956) etc., see monograph O (2001)

3 ICSA Applied Statistics Symposium 3 Nonparametric likelihood ratios R(F ) = L(F ) L(F n ) = n i=1 nw i where w i = F {X i }. Profile likelihood Statistic T = T (F ), true value τ 0 = T (F 0 ), NPMLE T = T (F n ) R(τ) = max{r(f ) T (F ) = τ} When do we get 2 log(r(τ 0 )) d χ 2? IE, a Wilks like result without assuming a parametric form. Then C = {τ R(τ) η} is an approximate confidence region.

4 ICSA Applied Statistics Symposium 4 For the mean T (F ) = x df (x), ie µ = E(X) Degeneracy R ( ) (1 ɛ)f n + ɛ1 x 1 as ɛ 0 any x For small enough ɛ > 0, (1 ɛ) X + ɛx C {µ R(µ) η} Letting x we get C = R d

5 ICSA Applied Statistics Symposium 5 Non-degeneracy For the mean, restrict F to have support in a bounded set B R d. It is enough to take B = B n chull(x 1,..., x n ) B n grows with n Upshot { R(µ) = sup (nw i ) i n i=1 w i x i = µ, w i > 0, i w i = 1, } Then 2 log(r(µ 0 )) d χ 2 (d) under moment conditions. O (1990)

6 ICSA Applied Statistics Symposium 6 Some good things about EL 1) (correct) data driven shape for confidence sets Hall 2) power optimality of tests Kitamura 3) allows side constraints O (1991), Qin & Lawless (1993) 4) Bartlett correctable DiCiccio, Hall & Romano (1991) 5) extends for a) censoring b) truncation c) biased sampling, 6) methods for a) time series Kitamura b) survey sampling Qin, Chen, Sitter,... Many more extensions S.-X. Chen; Hjort, McKeague & van Keilegom; Lahiri

7 ICSA Applied Statistics Symposium 7 Drawbacks 1) Region for mean bounded by convex hull of B n 2) Profiling the likelihood can be hard Hull Coverage of EL is at most coverage of hull of B n. This is a problem for small n and/or large d. Profiling Profiling is also hard for parametric likelihoods. Empirical likelihood is usually easy to compute for a fixed parameter vector.

8 ICSA Applied Statistics Symposium 8 Convexity { n R(µ) = sup (nw i ) i=1 Profile empirical likelihood w i 0, i w i = 1, i Confidence region for a mean w i (x i µ) = 0 } C(ɛ) = {µ R d R(µ) > ɛ} Nested inside convex hull C(ɛ) C(0) = convex hull(x 1,..., x n ), ɛ > 0

9 ICSA Applied Statistics Symposium 9 Adjusted EL coverage (extreme case) d = 4, n = 10 Normal Q Q Plot P P Plot EL Quantiles p values χ (4) Quantiles Uniform Quantiles Emerson & O (2009) Vertical asymptote from atom at + for 2 log R(µ 0 ).

10 ICSA Applied Statistics Symposium 10 Escape from the hull Idea: extend B n to ensure that µ B n Add an artificial point (undata) x n+1. Now, T (F ) = L(F ) = L(F ) = n+1 i=1 w i x i, n w i, i=1 n+1 i=1 w i. or, and, The second version is easier computationally and asymptotically the same (if x n+1 reasonable). Chen, Variyath & Abraham (2008) originate this approach.

11 ICSA Applied Statistics Symposium 11 Adjusted empirical likelihood Chen, Variyath & Abraham (2008) use x n+1 = µ a n ( x µ), a n = log(n)/2 a n = o p (n 2/3 ) preserves 1st order asymptotics Note: new point x n+1 depends on µ Now µ is between x and x n+1 : Hull of x 1,..., x n1 contains µ µ = x n+1 + a n x 1 + a n

12 ICSA Applied Statistics Symposium 12 Not all is well yet Let R be adjusted profile empirical likelihood. Then: [ ( ) (n + 2 log R 1)an (µ) 2 n log n(a n + 1) ( )] n log a n + 1 which is bounded, even if µ. Opposite problem from log R(µ) which diverged at finite µ. Instead of a bounded 100% region we can get all of R d at less than 100% confidence. Extreme example ctd. n = 10, d = 4, 88.1% region is R 4.

13 ICSA Applied Statistics Symposium 13 Coverage (extreme case) d = 4, n = 10 Normal Q Q Plot P P Plot AEL Quantiles p values χ (4) Quantiles Uniform Quantiles Emerson & O (2009)

14 ICSA Applied Statistics Symposium 14 Balanced adjusted empirical likelihood Dissertation: Emerson (2009) 1) Add 2 points x n+1 and x n+2 2) (x n+1 + x n+2 )/2 = x (preserving sample mean) 3) farther new points if µ x is a direction where the sample varies a lot Add points x n+1 = µ sc u u x n+2 = 2 x µ + sc u u where u = x µ c u = (u T S 1 u ) 1/2 x µ S = 1 n (x i x)(x i x) T s 1.9 n 1 i=1 Choice of s is based on empirical work. The best s depends (weakly) on d.

15 ICSA Applied Statistics Symposium 15 Related Independently Liu & Chen (2009) also added 2 points. Their 2 points were designed to improve Bartlett correction. Ours were tuned to give good small sample coverage in high dimensions.

16 ICSA Applied Statistics Symposium 16 Invariance Let A R d d be non-singular. Set x i = Ax i and µ = Aµ. Let C be the balanced adjusted empirical likelihood region for µ 0 based on x 1,..., x n. Let C be the balanced adjusted empirical likelihood region for µ 0 = Aµ 0 based on x 1,..., x n. Then µ C µ C. Emerson & O (2009) Proposition 4.1. Hotelling s T 2 and the original EL are also invariant this way.

17 ICSA Applied Statistics Symposium 17 Avoiding the boundedness Recall 2 log R was bounded. The new criterion 2 log R is unbounded. The ultimate cause is that x n+1 µ is proportional to x µ in AEL but is of constant order in BAEL The larger x n+1 µ in AEL means that less weight needs to go there. Less weight there allows more weight on the other n points and a higher likelihood.

18 ICSA Applied Statistics Symposium 18 Connection to T 2 Recall x n+1 = µ sc u u x n+2 = 2 x µ + sc u u, where u = x µ x µ and c u = (u T S 1 u ) 1/2. Theorem 4.2 Emerson & O (2009) lim s 2ns 2 ( ) (n + 2) 2 2 log R (µ) = T 2 (µ)

19 ICSA Applied Statistics Symposium 19 Quantile Quantile Plots d = 4, n = 10 Normal t(3) Double Exponential Empirical Quantiles Empirical Quantiles Empirical Quantiles Chi Square (4) Quantiles Uniform Chi Square (4) Quantiles Beta(0.1, 0.1) Chi Square (4) Quantiles Exponential(3) Empirical Quantiles Empirical Quantiles Empirical Quantiles Chi Square (4) Quantiles F(4, 10) Chi Square (4) Quantiles Chi square(1) Chi Square (4) Quantiles Gamma(1/4, 1/10) Empirical Quantiles Empirical Quantiles Empirical Quantiles Emerson & O (2009) Chi Square (4) Quantiles Chi Square (4) Quantiles Chi Square (4) Quantiles

20 ICSA Applied Statistics Symposium 20 Comments 1) More examples in the article 2) Good calibration for distributions with shorter tails 3) High kurtosis is harder 4) Even there the calibration is almost linear so a Bartlett correction could help a lot 5) Exact nonparametric CI.s for the mean are unobtainable Bahadur & Savage (1956)

21 ICSA Applied Statistics Symposium 21 Now, regression y x T β, x R d y R Estimating equations E ( x(y x T β) ) = 0 Normal equations n x i (y i x T i β) = 0 R d i=1 In principle we let z i = z i (β) x i (y i x T i β) Rd, adjoin z n+1 and z n+2, and carry on. residuals ɛ = (y x T β) are uncorrelated with x. They have mean zero too, when as usual, x contains a constant.

22 ICSA Applied Statistics Symposium 22 Regression hull condition { n R(β) = sup (nw i ) i=1 w i 0, n w i = 1, i=1 } n w i x i (y i x T i β) = 0 i=1 P = P(β) = {x i y i x T i β > 0} N = N (β) = {x i y i x T i β < 0} x with pos resid x with neg resid Convex hull condition O (2000) chull(p) chull(n ) = β C(0) For x i = (1, t i ) T R 2 P and N are intervals in {1} R.

23 ICSA Applied Statistics Symposium 23 Converse Suppose that τ {t 1,..., t n } and 1 t i > τ Sign(y i β 0 β 1 t i ) = 1 t i < τ Suppose also that Then i w i 1 (y i β 0 β 1 t i ) = t i 0 0 w i (y i β 0 β 1 t i )(t i τ) = 0 i But (y i β 0 β 1 t i )(t i τ) > 0 i Therefore the hull condition is necessary.

24 ICSA Applied Statistics Symposium 24 Example regression data y x Y = β 0 + β 1 X + σɛ β = (0, 3) T, σ = 1 β solid ˆβ dashed

25 ICSA Applied Statistics Symposium 25 Example regression data y x Red line is on boundary of set of (β 0, β 1 ) with positive empirical likelihood

26 ICSA Applied Statistics Symposium 26 Example regression data y x Another boundary line.

27 ICSA Applied Statistics Symposium 27 Example regression data y x Yet another boundary line. Left side has positive residuals; right side negative. Wiggle it up and point 3 gets a negative residual = ok. Wiggle down = NOT ok.

28 ICSA Applied Statistics Symposium 28 Example regression data y x All the boundary lines that interpolate two data points. They are a subset of the boundary.

29 ICSA Applied Statistics Symposium 29 Some regression parameters on the boundary β LS true β 1 Boundary points (β 0, β 1 ). Region is not convex. It is convex in β 0 (vertical) for fixed β 1 (horizontal).

30 ICSA Applied Statistics Symposium 30 What is a convex set of lines? convex set of (β 0, β 1 )? convex set of (ρ, θ)? (polar coordinates) convex set of (a, b) (ax + by = 1)?

31 ICSA Applied Statistics Symposium 31 Polar coordinates of a line y = mx + b y r θ x

32 ICSA Applied Statistics Symposium 32 Boundary pts in polar coords Some boundary points (polar coords) angle true radius Not convex here either.

33 ICSA Applied Statistics Symposium 33 Intrinsic convexity There is a geometrically intrinsic notion for a convex set of linear flats. J. E. Goodman (1998) When is a set of lines in space convex? Maybe... that can support some computation. Dual definition The set of flats that intersects a convex set C R d is a convex set of flats. So is the set of flats that intersect all of C 1,..., C k R d for convex C j.

34 ICSA Applied Statistics Symposium 34 Thanks Sarah Emerson Invitation: Heping Zhang Contacts: Mingxiu Hu, Tianxi Cao, Hongliang Shi & Naitee Ting NSF DMS