Asymptotic Methods in Statistical Inference - Statistics Centre

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218 under the two parameterizations for finite depends on how the nuisance parameter is estimated. In particular, using the restricted MLE ˆ in both instances results in different forms. • Regression: In (linear or nonlinear) regression with Normal errors the LR test of a linear hypothesis becomes the usual ‘drop in sum of squares’ F-test: = − , − where the ‘full’ model contains regression parameters, the hypothesized model contains − and are the minimum sums of squares of residuals in the two models. (In a linear model ∼ − .) To see this consider a general regression model Y ×1 = η (β)+ε ∼ ³ η (β) 2 I ´ (In linear regression η (β) =Xβ.)
219 The parameter vector is θ = ³ β 2´← ←1 and the likelihood function is (θ|y) = ³ ( ) 2 2´−2 ||y − η (β) ||2 exp − 2 2 with log-likelihood (apart from additive constants) (θ) =− 2 log ||y − η (β) 2 ||2 − 2 2 This is maximized over β by the LSE estimator ˆβ minimizing and then over 2 by (β) =||y − η (β) || 2 ³ˆβ´ ˆ 2 = = The maximized log-likelihood is ³ˆθ´ ³ = − log ˆ 2 +1´ . 2 A linear hypothesis on the ’s can always be written (after a linear reparameterization) in the form Ã ! Ã ! β(1) ← − β(1) β = = β (2) ← 0
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STATISTICS 665 ASYMPTOTIC METHODS I
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II LARGE-SAMPLE INFERENCE 42 5 Intr
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V LIKELIHOOD METHODS 169 19 Maximum
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Part I PROBABILISTIC PRELIMINARIES
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The ‘rate’ of convergence refer
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11 imagine ‘large’ but remain
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13 • For the ( ) distribution,
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15 —Corollary1: → If h (
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17 2. Slutsky’s Theorem; conseque
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— By the CLT, − q (1 − ) 1
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21 • Corollary 1: If → the
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23 • First an application. Let 2
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25 with d.f. (), then there is a
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27 next lecture), = 1− " ( )+
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29 • CLT via the Edgeworth expans
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31 (why?) with ‘cumulant generati
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Thus in (3.2), () () isthec.f.of
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35 • Delta method. Suppose that
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37 • Uniformity—read§2.6 • C
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39 suppose that → 0. Then X =1
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41 • Under this condition ˆ is (
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43 5. Introduction to asymptotic te
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45 • We often work instead with t
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47 • Two-sample problems. Suppose
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49 • Example 2: 1 ∼ P(),
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51 term is Ã √ ˆ ( ( 0 )
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53 • More useful is to study the
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55 • Example: t-test of a mean.
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57 • Efficacy. Test : = 0 vs.
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59 Put = − and test for tre
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61 7. Relative efficiency • Relat
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• The constraint (1) ∼ (2)
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65 =1. Theefficacy is = 0 q (0)
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67 • Example 3. In this example
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69 Proof of ‘ 1’: From 1 − (
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71 • We will consider only ‘lev
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73 • Example 1. Suppose we (mista
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75 To see this take 0 = 0 for simp
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77 The numerator of is normally d
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79 9. Confidence intervals • X =(
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81 • Example. 1 ∼ () ( 0);
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83 • A strong CI on a population
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85 • A more involved example of a
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87 We exhibit 1 () 2 () → 1−
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10. Point estimation; Asymptotic re
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91 Recall that if 0 () 6= 0 (assum
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93 where (with being a bound on |
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• Asymptotic Relative Efficiency.
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97 0.0 0.1 0.2 0.3 0.4 0.5 0.6 -3 -
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99 that then √ ³ ¯ − ´
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101 — This is also the ARE of the
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103 We minimize Z h 2 ()+2 2 ()
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105 In a broad class of cases, comp
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107 The ARE of the estimates consid
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109 We have lim →∞ { ∗ () −
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111 13. Random vectors; multivariat
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113 • X () () → c (constant)
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115 • Multivariate normality. We
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117 • If X ∼ (μ Σ) then an
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119 14. Multivariate applications
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121 One can eliminate the last elem
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123 have = (|)+ ¯ (| ¯) =
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125 at = 1. In this latter case Ã
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127 Proof: Write Then +1 = − X
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129 15. Expectation functionals; U-
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131 • A closely related estimate,
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1≤≤ 133 — e.g. = 2; let =
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135 • Rao-Blackwell Theorem: We c
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137 • Here we get the variance of
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139 16. Asymptotic normality of U-s
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141 Under , ( + ) = () ∼ (0 1)
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143 • Proofofclaim1: [( − ) |
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145 • V-statistics. Recall that i
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147 17. Influence function analysis
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149 By using the fact that | 2 −
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151 — e.g. If ( )= [], then ³
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153 By the CLT and Slutsky’s theo
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But −1 X =1 155 ( ≥ ) =
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157 18. Bootstrapping • Suppose t
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159 • In each of the examples abo
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161 • Example 2. ( )=bias. Writ
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163 • For the measure (18.1) we t
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165 (True for functionals (·) con
Page 167 and 168: 167 • Indeed, since ( ) has plu
Page 169 and 170: 169 Part V LIKELIHOOD METHODS
Page 171 and 172: 171 By this, for large samples and
Page 173 and 174: 173 • Assume: (C1) Identifiabilit
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Page 179 and 180: 179 • Information Inequality. (Cr
Page 181 and 182: 181 • Example: 1 the indicator
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Page 187 and 188: 187 Claim: (i) (ii) (iii) 0 ( 0 )
Page 189 and 190: 189 Define the ‘scores’ ( −
Page 191 and 192: 191 • The MLE is not the only eff
Page 193 and 194: 193 The variance-minimizing choice
Page 195 and 196: 195 • Multiparameter likelihood e
Page 197 and 198: 197 • Efficiency. Under appropria
Page 199 and 200: 199 asymptotic normality of the sam
Page 201 and 202: 201 • Likelihood ratio test. Put
Page 203 and 204: 203 and 000 ( ∗∗ ) is (1)
Page 205 and 206: 205 • Example. 1 ∼ Logistic,
Page 207 and 208: 207 • Multiparameter inferences.
Page 209 and 210: 209 For testing, I( 0 ) is replaced
Page 211 and 212: 211 24. Examples Example: 1 ∼
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Page 215 and 216: 215 • The likelihood ratio statis
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Page 221 and 222: 221 25. Higher order asymptotics
Page 223 and 224: 223 As in Lecture 10, cov h i 2 h
Page 225 and 226: 225 • The normal approximation, a
Page 227 and 228: 227 By uniqueness of m.g.f.s, (;
Page 229 and 230: 229 • Example 2. Let = P 1 w
Page 231: 231 • The error of Saddlepoint
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