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Dehai News Mailing List Archive As Aˆ A Aˆ A Aˆ As Aˆa µaˆ As A A ˆa Aˆ Aˆ As Aˆ C Aˆµa As A Aœ Aœ Aˆ Aˆµ Aœ Aœ A A A
Dehai News Mailing List Archive Aˆ A Aˆ A As A A A Aˆµ As A As A Aˆ As Aˆˆas As A A As Aˆª Aˆ A Aˆ 35 A Aˆ A µ Aˆ Aˆ Aˆµaˆ A µ Aˆƒ Aˆ A As As A As A Aˆ Aœza As Aœ As
Dehai News Mailing List Archive Tigrigna Voanews Com A ªa As As A µa Aœµa Aˆ Aˆµ As Aˆ A µ A Aˆ A µaˆ A A Aˆ A A Aˆˆa Z Aˆ A µaˆ As A A Aˆ Aˆ A As A µ As Aˆa µaˆ As As A µa Aœµa As As A A Aˆ As Aœ A As A Aˆ A As A
Dehai News Mailing List Archive Tigrigna Voanews Com A ªa As As A µa Aœµa Aˆ Aˆµ As Aˆ A µ A Aˆ A µaˆ A A Aˆ A A Aˆˆa Z Aˆ A µaˆ As A A Aˆ Aˆ A As A µ As Aˆa µaˆ As As A µa Aœµa As As A A Aˆ As Aœ A As A Aˆ A As A
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The_mute_savior_and_the_clueles_ñͨ_ñͨBOOKMOBI½_ H1 7Æ =º D Kù TÏ e‹ mí vq ~½ †³ ŽÂ –ë Ÿ §Œ ¯©"·É$À &Çë(Ð*Øn,à è’0ð°2øº4 6 X8 J s !Ç> *@@ 2¨B yD BÙF KH SeJ ßL d N lfP tÇR }T ÅV ÄX – Z ž \ ¦¬^ ®ý` ·rb ¿åd Çòf Ïûh ؈j á l éMn ðÈp ù^r ùt ,v 0x ½z ‹ Ž~ †€ %b‚ (Þ„ )Ò† Öˆ ¾Š /ŽŒ /¢Ž 0¢. µ 8ó2éÁÎVBê~!¼× íô ÓLÉ ©€7Ì Q9 é©Èþè9 #Óltë &Ê Š2Å› ’ìz³1嘞¤’Iäš²U ì µ8W‰Z¼ª3Ø£B4טQE ¸QE ¬ G¿z( ëý1fºKû9šËTŒmŽî óc9Ú㣦 „ñžF è7âY. ∂µ2 = −µ−2 i=1 x i < 0 Thus there is a local maximum at µ = ¯x We then note that as µ → 0 or µ → ∞, the loglikelihood ‘(µ;x) approaches −∞ Thus µ = ¯x is a global maximum, and the maximum likelihood estimate of µ is ˆµ = ¯x The maximum likelihood estimator in this example is then ˆµ(X) = X¯ Since µ is the.
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X y x y x y G t x e G t x y µ µ 1 y x yx Ge G t x yt µ µ Then to find the from ECON 221 at Concordia University. ÿd“ ø K ¸Ô t Ð Albert Park þ ¡¶ d ´ z ¡ Üd R Q T 8 è p H A É Ý—— H A y` a K梲 Ò !u 0² Ò E § 0Ø æ¢ ¡ ^ Kµæd² Ò Ï H A IKëè z ¡ !. Suppose that E(X)=µ, Var(X)=s2 Then (i) E(Yn)=µn (ii)If µ 6= 1, then Var(Yn)= s2µn¡1(1¡µn) (1¡µ) If µ =1 then Var(Yn)=ns 2 Proof Was given in lectures (and a different proof can be found in Notes 4) Some additional properties of conditional expectations 1 If X and Y are independent rv’s then E(XjY)=E(X) Proof.
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CovX,Y=E(X −µ X)(Y −µ Y)=EXY−µ Xµ Y IfX tendstobelargewhenY islarge,thecovariancewillbepositive 2 If two random variables are independent, their covariance is zero However, the opposite is not (quite) true two random variables can have zero covariance without being independent 3 Thecorrelation coefficient ofX andY is ρ. Definition 2 Let X and Y be random variables with their expectations µ X = E(X) and µ Y = E(Y), and k be a positive integer 1 The kth moment of X is defined as E(Xk) If k = 1, it equals the expectation 2 The kth central moment of X is defined as E(X − µ X)k If k = 2, then it is called the variance of X and is denoted by var(X). Compilatio_de_CatilinaR –R –BOOKMOBI ñ ù 2ú 8ë = A¤ Eà JO N¾ Uþ ž dö lm sø {n ƒb ŠÉ ’× ™à"¡Ò$© &°Ã(¸ß*¿Ï,ÇÏ¢0×52Þä4åã6îŽ8ö¨ý» Â> Ã@ ØB ïD #ïF ®H 3 J ÇL BòN JFP QóR Z T aGV hMX oðZ x \ )^ ‡a` Vb ˜%d f § h ¯÷j ·ãl ¿Yn Æ›p Íìr ÕÊt Ýîv åõx îÍz ÷ð ÿ´~ 4€ Å‚ á„ ð† %þˆ / Š 8PŒ A Ž Hõ Rk’ Z.
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