STATISTICS 512 TECHNIQUES OF MATHEMATICS FOR ...

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34 the mean squared error. Wehave Ã [z] =Q 0 Q 0 [y] = 1 Q 1 R 1 θ Q 0 2 Q 1R 1 θ ! = Ã R1 θ 0 ! and, using “cov[Ay] =Acov[y] A 0 ”(how?) we get cov [z] =Q 0 cov [y] Q = Q 0 2 IQ = 2 I; hence the elements +1 of z 2 have mean zero and [ ]= h 2 i = 2 . Thus ˆ 2 is unbiased: hˆ 2i = ⎡ ⎣P +1 2 − ⎤ ⎦ = 2 • Unrelated but nonetheless useful facts: inverses and determinants of matrices in block form. If P and Q are nonsingular, then det Ã P R S Q ! = |P|·|Q − RP −1 S| = |Q|·|P − SQ −1 R|
35 and = Ã P ⎛ ⎜ ⎝ R S Q ! −1 ³ P − SQ −1 R´−1 −P −1 S· ³ Q − RP −1 S´−1 − ³ Q − RP −1 S´−1 · RP −1 How? Verify Ã !Ã I −SQ −1 P S 0 I R Q Ã P − SQ = −1 ! R 0 etc. 0 Q Example: det Ã I 1 1 0 −1 ! !Ã ³ Q − RP −1 S´−1 I 0 −Q −1 R I = |I |·|− 1 − 1 0 1| = −1 − ! ⎞ ⎟ ⎠
Page 1 and 2: STATISTICS 512 TECHNIQUES OF MATHEM
Page 3 and 4: II LIMITS, CONTINUITY, DIFFEREN- TI
Page 5 and 6: 19 Numerical optimization: Steepest
Page 7 and 8: 7 1. Introduction; matrix manipulat
Page 9 and 10: - is of rather limited usefulness.
Page 11 and 12: 11 — Block matrices ... a particu
Page 13 and 14: 13 for a random matrix X. You shoul
Page 15 and 16: 15 Here the observations (rows) hav
Page 17 and 18: 17 3. Identity element: There is 0
Page 19 and 20: 19 — Fact 1: Every vector space h
Page 21 and 22: 21 3) Used often: (A 0 A)=(A). Proo
Page 23 and 24: 23 areallsolvable. Wewrite[b 1 ··
Page 25 and 26: 25 • Angle between nonzero vecto
Page 27 and 28: 27 (why?). Geometrically, an orthog
Page 29 and 30: 29 orthogonal unit vectors q 1 q h
Page 31 and 32: 31 4. LSEs; Spectral theory • Rec
Page 33: 33 (ii) In terms of QR-decompositio
Page 37 and 38: 37 • Now suppose that M is symmet
Page 39 and 40: 39 • Spectral Decomposition Theor
Page 41 and 42: 41 5. Examples & applications Conse
Page 43 and 44: 43 This is the ellipsoid in R with
Page 45 and 46: 45 • If H is idempotent then (i)
Page 47 and 48: 47 and similarly define 2 2 as the
Page 49 and 50: 49 Application 2. By the Cauchy-Sch
Page 51 and 52: 51 6. Limits; continuity; probabili
Page 53 and 54: 53 — ⊂ R is closed if it cont
Page 55 and 56: 55 bounded, q say =(0). It can be
Page 57 and 58: 57 7. Random variables; distributio
Page 59 and 60: 59 • Since the set = (−∞] is
Page 61 and 62: 61 • Jensen’s Inequality: If :
Page 63 and 64: 63 • If → and the function
Page 65 and 66: 65 • Linearity, product, quotient
Page 67 and 68: 67 • Taylor’s Theorem:“Suffic
Page 69 and 70: 69 We want to show that (), which
Page 71 and 72: 71 9. Applications: transformations
Page 73 and 74: 73 monotonic. Example: suppose ∼
Page 75 and 76: 75 Now suppose that → and (ac
Page 77 and 78: 77 This can make it problematic to
Page 79 and 80: 79 10. Sequences and series • Con
Page 81 and 82: 81 Then for each , sup | () − ()
Page 83 and 84: 83 Thus a convergent sequence (i.e.
Page 85 and 86:
85 Then for 0 we have 0 = + X
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87 11. Power series; moment and pro
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89 • If P ∞ =0 converges fo
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91 • By (ii), we can repeat the p
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93 • The moment generating functi
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95 12. Branching processes • Impo
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97 Considering the probabilities of
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99 Since = ( −1 )and is continu
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101 P(N
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103 Then clearly () ≤ () ≤
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105 • Monotonic, bounded function
Page 107 and 108:
107 • Now define () = Z ()
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109 • Improper Riemann integrals,
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111 14. Riemann and Riemann-Stieltj
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113 • A generalization of the Rie
Page 115 and 116:
115 • Improper R-S integrals defi
Page 117 and 118:
117 • Cauchy-Schwarz inequality:
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119 constant’) ∈ [0 1) as →
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15. Moment generating functions; Ch
Page 123 and 124:
123 • Suppose ∼ (0 1), with p.
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125 • Chebyshev’s Inequality fu
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127 Let be fixed but arbitrary. Ex
Page 129 and 130:
129 • Slutsky’s Theorem: If
Page 131 and 132:
131 Part IV MULTIDIMENSIONAL CALCUL
Page 133 and 134:
133 • Derivatives. Put e = (00 1
Page 135 and 136:
135 — If = 1 then the Jacobian m
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137 1. If the partial derivatives o
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139 quantile function. Differentiat
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141 17. Implicit Function Theorem;
Page 143 and 144:
143 • The Inverse Function Theore
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145 • Example. Write the characte
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147 • Often we seek extrema of mu
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149 under which the satisfaction of
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151 Since x 2 is a stationary point
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153 If sup or equivalently if ()
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155 Here and elsewhere the assumpti
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157 • Example: Let be independe
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159 order that 2 () integrate to 1
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161 Note that x ¯ ¯ = ¯¯¯¯¯
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163 with = 0 to minimize (by tria
Page 165 and 166:
165 —Example:solve () =log − 1=
Page 167 and 168:
167 regression, so I’ll illustrat
Page 169 and 170:
169 Assuming convergence, the limit
Page 171 and 172:
171 • For i.i.d. observations wit
Page 173 and 174:
173 • The MLE is generally obtain
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175 so that ³ covθ h˙(θ) i =´
Page 177 and 178:
177 We have (by the WLLN) that X Ã
Page 179 and 180:
with gradient ˙(θ) = Ã − +
Page 181 and 182:
181 • Method of moments: Define p
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• The limit of the NR-process is
Page 185 and 186:
185 • Now let (X) be any unbiased
Page 187 and 188:
187 22. Minimax M-estimation I •
Page 189 and 190:
189 • The asymptotic variance doe
Page 191 and 192:
191 • We will show that such a pa
Page 193 and 194:
193 • By the Lemma, () in(22.3)is
Page 195 and 196:
195 • By comparison with (22.4) w
Page 197 and 198:
We note that and () = − 0 = −
Page 199 and 200:
199 • A solution (there are three
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• The asymptotic normality result
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203 where the infimum is over all s
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205 ∪ =1 ;inthiscasedefine R =
Page 207 and 208:
207 • If is Lebesgue measure the
Page 209 and 210:
209 • The expected value of a r.v
Page 211:
211 • It was stated above that if
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