Chapter 4: Asymptotic Properties of the MLE (Part 2)

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1 Chapter 4: Asymptotic Properties of the MLE (Part 2) Daniel O. Scharfstein 09/24/13 1 / 1

2 Example Let {(R i, X i ) : i = 1,..., n} be an i.i.d. sample of n random vectors (R, X ). Here R is a response indicator and X is a covariate. We assume that logit P[R = 1 X ] = α + βx and assume that X is normally distributed with mean µ and variance σ 2. So, our probability model has four parameters, θ = (α, β, µ, σ 2 ). Let θ 0 = (α 0, β 0, µ 0, σ0 2 ) denote the true value of θ. 2 / 1

3 Likelihood L(θ; x, r) = n i=1 1 σ 2π exp( 1 2σ 2 (x i µ) 2 ) exp(r i(α + βx i )) 1 + exp(α + βx i ) The log-likelihood for an individual is l(θ; x, r) log(σ) 1 2σ 2 (x µ)2 +r(α+βx) log(1+exp(α+βx)) 3 / 1

4 Score The score vector for an individual is r ψ(x, r; θ) = = x(r l(θ;x,r) α l(θ;x,r) β l(θ;x,r) µ l(θ;x,r) σ 2 exp(α+βx) 1+exp(α+βx) exp(α+βx) 1+exp(α+βx) ) x µ σ (x µ)2 2σ 2 2σ 4 The score equations for the full sample are: n r i exp(α + βx i) 1 + exp(α + βx i ) = 0 (1) i=1 n i=1 n i=1 n i=1 x i (r i exp(α + βx i) 1 + exp(α + βx i ) ) = 0 (2) x i µ σ 2 = 0 (3) 1 2σ 2 + (x i µ) 2 2σ 4 = 0 (4) 4 / 1

5 MLE Note that Equations (3) and (4) can be solved explicitly to get solutions for ˆµ and ˆσ 2, i.e., ˆµ = 1 n n x i and ˆσ 2 = 1 n i=1 n (x i ˆµ) 2 i=1 The solutions for ˆα and ˆβ are obtained by solving Equations (1) and (2). Requires iterative technique such as Newton-Raphson (more later). 5 / 1

6 Observed Information Matrix Let p i (α, β) = exp(α+βx i ) 1+exp(α+βx i ) and q i(α, β) = 1 p i (α, β), Now, nj n (θ) = [ Jn1 (α, β) 0 0 J n2 (µ, σ 2 ) ] where [ n J n1 (α, β) = i=1 p i(α, β)q i (α, β) n i=1 x ip i (α, β)q i (α, β) n i=1 x ip i (α, β)q i (α, β) n i=1 x i 2p i(α, β)q i (α, β) ] and J n2 (µ, σ 2 ) = [ n 1 σ 1 2 σ 4 n i=1 (x i µ) n σ 4 i=1 (x i µ) n + 3 n 2σ 3 2σ 5 i=1 (x i µ) 2 ] 6 / 1

7 Assumptions Assume that α K 1, β K 2, µ K 3, and K 4 σ 2 K 5, where K 1,..., K 5 are finite positive quantities. Assume that θ 0 (truth) belongs to the interior of Θ. 7 / 1

8 Regularity Conditions Condition i) is satisfied by assumption. Condition ii) is satisfied since log p(x, r; θ) is continuous at each θ for all < x < and r = 0, 1. Now, we need to show condition iii) that log p(x, r; θ) d(x, r) for all θ Θ and E θ0 [d(x, R)] <. log p(x, r; θ) = 1 1 log(2π) log(σ) 2 2σ (x 2 µ)2 + r(α + βx) log(1 + exp(α + βx)) By the triangle inequality, we know that log p(x, r; θ) 1 2 log(2π) + log(σ) + 1 2σ (x 2 µ)2 + r(α + βx) + log(1 + exp(α + βx)) 8 / 1

9 Inequalities x 1 + x 2 log(1 + exp(α + βx)) 0 log(1 + exp(α + βx)) log(1 + exp( α + β x )) 1 + log(exp( α + β x )) = 1 + α + β x 1 + K 1 + K 2 (1 + x 2 ) log(σ) 1 2 max( log(k 4), log(k 5 ) ) 1 2σ 2 (x µ)2 1 2σ 2 x µ2 µ x + σ2 2σ 2 x 2 + K 3 (1 + x 2 ) + K 3 2 2K 4 K 4 2K 4 9 / 1

10 Inequalities r(α + βx) α + β x K 1 + K 2 (1 + x 2 ) log(1 + exp(α + βx)) 1 + K 1 + K 2 (1 + x 2 ) 10 / 1

11 Regularity Conditions Let d(x, r) = 1 2 log(2π) max( log(k 4), log(k 5 ) ) + x 2 2K 4 + K 3 K 4 (1 + x 2 ) + K 2 3 2K 4 + K 1 + K 2 (1 + x 2 ) K 1 + K 2 (1 + x 2 ) Now, E θ0 [X 2 ] = µ σ2 0 <. This implies that E θ0 [d(x, R)] <. 11 / 1

12 Regularity Conditions For condition iv), we have to show that p(x, r; θ) is twice-continuously differentiable and p(x, r; θ) > 0 for θ in a neighborhood of θ 0. This follows simply from the fact that the density is made up of polynomial and exponentials for which derivatives exists. The density is clearly positive over the entire parameter space. 12 / 1

13 Regularity Conditions For condition v), we have to show that p(x,r;θ) θ e(x, r), where e(x, r)is integrable. Remember, and p(x, r; θ) = 1 σ 2π exp( 1 2σ 2 (x exp(r(α + βx)) µ)2 ) 1 + exp(α + βx) p(x, r; θ) θ = 1 σ exp( 1 2π 1 σ exp( 1 2π 1 σ exp( 1 2π 2σ 2 (x µ) 2 ) 2σ 2 (x µ) 2 ) r exp(r(α+βx))+(r 1) exp((r+1)(α+βx) (1+exp(α+βx)) 2 xr exp(r(α+βx))+x(r 1) exp((r+1)(α+βx) (1+exp(α+βx)) 2 (1+exp(α+βx)) { x µ 2 σ } 2 2σ (x µ) 2 ) exp(r(α+βx)) 2 1 σ exp( 1 2π 2σ (x µ) 2 ) exp(r(α+βx)) 2 (1+exp(α+βx)) { 1 2 2σ 2 + (x µ)2 σ 2 } 13 / 1

14 Regularity Conditions Now, p(x, r; θ) θ p(x, r; θ) + α p(x, r; θ) + β p(x, r; θ) p(x, r; θ) + µ σ 2 We want to bound each of the terms in the sum by a function of x and r which is integrable. To do this, note that 1 σ 2π exp( 1 2σ 2 (x µ)2 ) e (x) where e (x) = 1 2K4π 1 exp( 1 2K4π 1 exp( 1 2K4π 2K 5 (x K 3 ) 2 ) x > K 3 K 3 x K 3 2K 5 (x + K 3 ) 2 ) x < K 3 14 / 1

15 Regularity Conditions Now, p(x, r; θ) α e 1 (x) = 2e (x) p(x, r; θ) β e 2 (x) = 2xe (x) p(x, r; θ) µ e 3 (x) = e (x)( x + K 3 K4 2 ) p(x, r; θ) σ 2 e 4 (x) = e (x)( 1 2K5 2 + x 2 (1 + 2K 3 ) + 2K 3 + K 2 3 K 2 4 We know that p(x,r;θ) θ e(x) = e 1 (x) + e 2 (x) + e 3 (x) + e 4 (x). e(x) is integrable since each e i (x) (i = 1,..., 4) is integrable. ) 15 / 1

16 Regularity Conditions For condition vi), we need to show that I (θ 0 ) = E θ0 [ψ(x, R; θ 0 )ψ(x, R; θ 0 ) ] is non-singular. It would suffice to show that I (θ 0 ) was positive definite. That is, a I (θ 0 )a > 0 for all a such that a > 0. Alternatively, we can show that Var θ0 [a ψ(x, R; θ 0 )] > 0. Now, a ψ(x, R; θ 0 ) = a 1 (R exp(α 0 + β 0 X ) 1 + exp(α 0 + β 0 X ) ) + a 2 X (R exp(α 0 + β 0 X ) 1 + exp(α 0 + β 0 X ) ) + a 3 ( X µ 0 σ0 2 ) + a 4 ( 1 2σ0 2 + (X µ 0) 2 σ0 4 ) Var θ0 [a ψ(x, R; θ 0)] = E θ0 [Var θ0 [a ψ(x, R; θ 0) X ]]+Var θ0 [E θ0 [a ψ(x, R; θ 0) X ]] 16 / 1

17 Regularity Conditions Var θ0 [a ψ(x, R; θ 0 ) X ] = (a 1 + a 2 X ) 2 exp(α 0 + β 0 X ) (1 + exp(α 0 + β 0 X )) 2 E θ0 [a ψ(x, R; θ 0 ) X ] = a 3 ( X µ 0 σ0 2 ) + a 4 ( 1 2σ0 2 + (X µ 0) 2 σ0 4 ) Var θ0 [a ψ(x, R; θ 0 )] = E θ0 [(a 1 + a 2 X ) 2 exp(α 0 + β 0 X ) (1 + exp(α 0 + β 0 X )) 2 ] + Var θ0 [a 3 ( X µ 0 σ0 2 ) + a 4 ( 1 2σ0 2 + (X µ 0) 2 σ0 4 )] = E θ0 [(a 1 + a 2 X ) 2 exp(α 0 + β 0 X ) (1 + exp(α 0 + β 0 X )) 2 ] + a3 2 σ0 2 + a2 4 4σ 4 0 The variance can only equal zero if a 1 = a 2 = a 3 = a 4 = 0. So, I (θ 0 ) is positive definite. 17 / 1

18 Regularity Conditions Conditions vii) and viii) involve showing that the norm of the matrix of second derivatives of the log likelihood and likelihood are bounded by functions of x and r which are integrable. The norm of a matrix A = [a ij ] is equal to ( i,j a2 ij )1/2. For each of these matrices, it is sufficient to show that the absolute value of each of entries is bounded by an integrable function of x and r. The bounding techniques are very similar to those used to show condition v). 18 / 1

19 Asymptotic Variance of θ n I (θ 0 ) 1 = exp(α E θ0 [ 0+β 0X ) (1+exp(α 0+β 0X )) ] 2 E θ0 [X exp(α0+β0x ) (1+exp(α 0+β 0X )) ] E θ0 [X exp(α0+β0x ) (1+exp(α 0+β 0X )) ] 2 E θ0 [X 2 exp(α 0+β 0X ) (1+exp(α 0+β 0X )) ] σ σ / 1

20 Regular Estimators Suppose we want to estimate a real-valued function of θ, say g(θ), g( ) : R k R. Assume that g has continuous partial derivatives. Consider estimating g(θ 0 ) by g(ˆθ(x n )), where ˆθ(X n ) is the MLE. Assuming that the 8 regularity conditions hold, we know that n(ˆθ(x n ) θ 0 ) D N(0, I 1 (θ 0 )). By the multivariate delta method, we know that n(g(ˆθ(x n )) g(θ 0 )) D N(0, a(θ 0 ) I 1 (θ 0 )a(θ 0 )) where a(θ 0 ) is the gradient of g(θ) at θ / 1

21 Regular Estimators Consider an estimator, T (X n ), of g(θ 0 ) which is asymptotically normal and asymptotically unbiased, i.e., n(t (Xn ) g(θ 0 )) D N(0, V (θ 0 )) (5) It turns out that under some additional regularity conditions on T (X n ), we can shown that V (θ 0 ) a(θ 0 ) I 1 (θ 0 )a(θ 0 ) (6) 21 / 1

22 Regular Estimators Definition: A regular estimator T (X n ) of g(θ 0 ) which satisfies (5) with V (θ 0 ) = a(θ 0 ) I 1 (θ 0 )a(θ 0 ) is said to be asymptotically efficient. If g(ˆθ(x n )) is regular, then we know that it is asymptotically efficient. For a long time, it was believed that the regularity conditions needed to make (6) hold involved regularity conditions on the density p(x; θ). This belief was exploded by Hodges. 22 / 1

23 Hodges Example of Super-Efficiency Suppose that X n = (X 1,..., X n ) where the X i s are i.i.d. Normal(µ 0, 1). We can show that I (µ 0 ) = 1 for all µ 0. Consider the following estimator of µ 0, { X ˆµ(X n ) = n X n n 1/4 0 X n < n 1/4 Suppose that µ 0 0. Then n(ˆµ(xn ) µ 0 ) = n(ˆµ(x n ) X n ) + (7) n( Xn µ 0 ) (8) By the CLT, we know that (8) converges in distribution to a N(0, 1) random variable. We can show that (7) converges in probability to zero. By Slutsky s theorem, we know that n(ˆµ(xn ) µ 0 ) D N(0, 1). So the estimator attains the efficiency bound. 23 / 1

24 Hodges Example of Super-Efficiency To show that (7) converges in probability to zero, note that P µ0 [ n(ˆµ(x n) X n) = 0] = P µ0 [ˆµ(X n) = X n] = P µ0 [ X n n 1/4 ] = P µ0 [ X n n 1/4 ] + P µ0 [ X n n 1/4 ] = P µ0 [ n( X n µ 0) n(n 1/4 µ 0)] + P µ0 [ n( X n µ 0) n( n 1/4 µ 0)] = Φ( n(n 1/4 µ 0)) + 1 Φ( n( n 1/4 µ 0)) = Φ(n 1/4 n 1/2 µ 0) + 1 Φ( n 1/4 n 1/2 µ 0) 1 24 / 1

25 Hodges Example of Super-Efficiency If µ 0 = 0, then we can show that n(ˆµ(x n ) µ 0 ) P 0 or n(ˆµ(xn ) µ 0 ) D N(0, 0). This violates (6). To show this result, we note that P µ0 [ n(ˆµ(x n ) µ 0 ) = 0] = P µ0 [ˆµ(X n ) = 0] = P µ0 [ X n < n 1/4 ] = P µ0 [ n 1/4 < X n < n 1/4 ] = P µ0 [ n 1/4 < n X n < n 1/4 ] 1 25 / 1

26 Remarks about Super-Efficiency The problem here is not due to irregularities of the density function, but due to the partialness of our estimator to µ 0 = 0. This example shows that no regularity conditions on the density can prevent an estimator from violating (6). This possibility can only be avoided by placing restrictions on the sequence of estimators. LeCam (1953) showed that for any sequence of estimators satisfying (5), the set of points in Θ violating (6) has Lebesgue measure zero. 26 / 1

27 Regular Estimators When you first discussed estimators, it was argued that it was important to rule out impartial estimators, i.e., estimators which favored some values of the parameters over others. In an asymptotic sense, we may want our sequence of estimators to be impartial so that we rule out estimators like the one presented by Hodges. Toward this end, we may restrict ourselves to regular estimators. A regular sequence of estimators is one whose asymptotic distribution remains the same in shrinking neighborhoods of the true parameter value. 27 / 1

28 Regular Estimators More formally. consider a sequence of estimators {T (X n )} and a sequence of parameter values {θ n } so that n(θ n θ 0 ) is bounded. T (X n ) is a regular sequence of estimators if converges to the same limit as P θn [ n(t (X n ) g(θ n )) a] P θ0 [ n(t (X n ) g(θ 0 )) a] for all a. For estimators that are regular and satisfy (2), (3) holds. When we talk about influence functions, we will formally establish this result. 28 / 1

29 Is the Hodge s Estimator Regular? Suppose that µ 0 = 0. We know that the limiting distribution of n(ˆµ(xn ) µ 0 ) is a degenerate distribution with point mass at zero. Consider the sequence µ n = τ/ n, where τ is some positive constant. Note that n(µ n µ 0 ) τ. Now, we can show that n(ˆµ(xn ) µ n ) P(µn) τ. To see this, not that P µn [ n( ˆµ(X n) µ n) = τ] = P µn [ n( ˆµ(X n) τ/ n) = τ] = P µn [ ˆµ(X n) = 0] = P µn [ X n < n 1/4 ] = P µn [ n 1/4 < X n < n 1/4 ] = P µn [ n( n 1/4 µ n) < n( X n µ n) < n(n 1/4 µ n)] = Φ( n( n 1/4 µ n)) Φ( n(n 1/4 µ n)) = Φ( n 1/4 τ) Φ(n 1/4 τ) 1 29 / 1

30 Is the Hodge s Estimator Regular? So, the limiting distribution of n(ˆµ(x n ) µ n ) is also degenerate, but with point mass at τ. This is a different limiting distribution than that of n(ˆµ(x n ) µ 0 ). Therefore, ˆµ(X n ) is not regular. 30 / 1

Chapter 4: Asymptotic Properties of the MLE

Chapter 4: Asymptotic Properties of the MLE Daniel O. Scharfstein 09/19/13 1 / 1 Maximum Likelihood Maximum likelihood is the most powerful tool for estimation. In this part of the course, we will consider