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N ·e− n i(y −µ 2τ2 =e− 1 2σ2 m j=1 x 2 j− 1 2τ2 n i=1 y 2 i µ σ2 m j=1 x µ τ2 n i=1 y −B(µ,σ 2,τ2), where B(µ,σ2,τ2) m 2 ln2πσ 2 n 2 ln2πτ 2 mµ2 2σ2 nµ2 2τ2 Notice that the joint pdf belongs to the exponential family, so that the minimal statistic for θPX = Y = pq pq −pq (b) What is Emax(X,Y)?BASIC STATISTICS 5 VarX= σ2 X = EX 2 − (EX)2 = EX2 − µ2 X (22) ⇒ EX2 = σ2 X − µ 2 X 24 Unbiased Statistics We say that a statistic T(X)is an unbiased statistic for the parameter θ of theunderlying probabilitydistributionifET(X)=θGiventhisdefinition,X¯ isanunbiasedstatistic for µ,and S2 is an unbiased statisticfor σ2 in a random sample 3
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M(n)(0) = E(), n ≥ 1 (8) The mgf uniquely determines a distribution in that no two distributions can have the same mgf So knowing a mgf characterizes the distribution in question If X and Y are independent, then E(es(XY )) = E(esXesY) = E(esX)E(esY), and we conclude that the mgf of an independent sum is the product of the individual mgfI è µ µ è ã Ô Þ s z ü x ê b q s ë d ^ s M s b q U d W Ù y  Á v R Q § Ý p d ^ s ® r y § È , b q O Z ^ s s y ² ½ s b q I u Ä r M s á S d } ´ § ( è O µ Ý a p y j y ) & u Ì , b d } j ^ y µ Ä Þ ° N & ° r & v, d j v ¼ y, 0 O ® s bè A Ó Q N p µ Ú n Ð c ô o õ ú 3 H = ç µ æ T Û d ù ç 8 & q Ð À 3 Þ Ì ¢ i ù p Þ Ì È ô `  ³ i Ý Ð y ì Ð z p þ ù ç µ æ T e µ þ Õ ä á 0 ¢ Ð ë W ù 0, e, p c ö i ï x ù p µ i â ù e p ã i Ý Ð M H ù µ c i p M d è



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Then M Y (t)=exp(t µ)exp( 1 2 t BDB t) andBDB issymmetricsinceDissymmetricSincetBDBt=uDu,whichisgreater than0exceptwhenu=0(equivalentlywhent=0becauseBisnonsingular),BDB is positivedefinite,andconsequentlyY isGaussian Conversely,supposethatthemoment± ( ± õ ú õ !1 v ec tor of consta n ts Find V (X c )Sho w y our w o rk T hi s is imp orta n t b ecause it tells us w e can a lw a y s pr etend the mea n eq uals ze ro when



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C » < ÷ \ » R Ò » ® þ x µ ® ½ ` R ¾ ¸ À µ Q £ & = R D ~ ª þ y Ç È X þ M 0 × è Ì 4 ' ) I = E ³ r X ¥ 4 { % ³ x x µ ® ½ ` R ¾ ¸ À µ £ I v Ë { x x µ ® ½ ` R ¾ ¸ À µ b m G Q ù B Ï x C ~ * N b O * N G Q ù O * %$ g V l & ÏLet ˜ C (f,µ,ν) = e f x µ x ν w µ w ν y µ ′ y ν ′ B k,r,t (mod B f 1 k,r,t), where B f 1 k,r,t is the twosided ideal generated by e f 1 a) The set {e f x µ x ν w µ w ν y µ ′ y ν ′ d (t) dx κ d (mod B f 1 k,r,t) (t,d,κ d) ∈ δ (f,µ ′,ν ′)} is a basis of ˜ C (f,µ,ν) b) As right B k,r,tmodules, ˜ C (fCase where n = 2, and Σ is diagonal, ie, x = x1 x2 µ = µ1 µ2 Σ = σ2 1 0 0 σ2 2 As we showed in the last section, p(x;µ,Σ) = 1 2πσ1σ2 exp − 1 2σ2 1 (x1 −µ1) 2 − 1 2σ2 2 (x2 −µ2) 2 (4) Now, let's consider the level set consisting of all points where p(x;µ,Σ) = c for some constant c ∈ R In particular, consider



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In A B E N O H A R U K A S ñ ï õ c ¾ i _  « ¿Ø°¼®Ý j T oshitaka K A T O *1 M asanobu IN O U E *1 Y uji Y A M A Z A K I *1 F um itaka IB A *2 N oriaki S H IO T A *3 *1T akenaka C orporation *2S anki E ngineering C oL td *3 K O B E L C O E C O S O L U T IO N S C o,L tdTitle Area CScience, Mathematics, & Technology xlsx Author kassn Created Date 7/1/ 552 PM(b) (7 points) Derive , the variance of U, in terms of b, and the covariance 2 σU 2, 2 σX σY σXY 2 σU = EU 2 − E(U)2 = EU2 because the second term is zero = E(Y − µ Y) 2 − 2b(X−µ x)(Y − µ Y) b 2(X−µ x) 2 = E(Y − µ Y) 2 − 2bE(X−µ x)(Y − µ Y) b 2E(X−µ x) 2 = 2 − 2b σY σXY b 2 2 σX (c) (6 points) Suppose I want to choose b in order to



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¾ Á » µ Ä Þ ¸ y Ù û = à Ñ è Ä ¡  t r Î ¢ Õ ê ¶ Å ñ Æ r M ë ê ñ à ñ ½ È è z Ï y Q v ^ q O d } ° ¦ ñ Ç ä ´ y A @ Ù v 2 d C } W z v ö 2(0 z ^ y ÿ b O ¤ Ö v u O è » r 2 " r X Q 9 ( ?µ θσ 2 /2 1 = EX = Ee Y = M Y (1) = e µ 2θ2σ 2 2 = EX 2 = Ee 2Y = M Y (2) = e (b) First, note that µ 2 σ 2 2 /(µ 1) = e It follows that a methodofmoments estimate for σ 2 is σˆ 2 = ln(ˆµ 2 /µˆ 2 1) where µˆ 1 = 1 n X i n i =1 µˆ 2 =U Å ½ s Ú è ñ ® v ï Å ½ v < à Å é Ñ b t u è O b j } t u y C N > y ;



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