Dr. Hackney STA Solutions pg 190

# Dr. Hackney STA Solutions pg 190 - 11-18 Solutions Manual...

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Unformatted text preview: 11-18 Solutions Manual for Statistical Inference 2 which is the usual bivariate normal likelihood except that we replace Sxx with Sxx + 0 . So the MLEs are the usual ones, and the EM iterations are x(t+1) n X ^ X ^ (t+1) = X + (t) ^ = x(t) = = (t) X Y (t) (t) (yn - y ) 2(t+1) (t+1) ^ Sxx + (1 - 2(t) )^X ^ n (t) Sxy (t) (t) 2(t) . 2(t+1) (Sxx + (1 - 2(t) )^X ^ Here is R code for the EM algorithm: )Syy nsim<-20; xdata0<-c(20,19.6,19.6,19.4,18.4,19,19,18.3,18.2,18.6,19.2,18.2, 18.7,18.5,18,17.4,16.5,17.2,17.3,17.8,17.3,18.4,16.9) ydata0<-(1,1.2,1.1,1.4,2.3,1.7,1.7,2.4,2.1,2.1,1.2,2.3,1.9,2.4,2.6, 2.9,4,3.3,3,3.4,2.9,1.9,3.9,4.2) nx<-length(xdata0); ny<-length(ydata0); #initial values from mles on the observed data# xmean<-18.24167;xvar<-0.9597797;ymean<-2.370833;yvar<- 0.8312327; rho<- -0.9700159; for (j in 1:nsim) { #This is the augmented x (O2) data# xdata<-c(xdata0,xmean+rho*(4.2-ymean)/(sqrt(xvar*yvar))) xmean<-mean(xdata); Sxx<-(ny-1)*var(xdata)+(1-rho^2)*xvar xvar<-Sxx/ny rho<-cor(xdata,ydata0)*sqrt((ny-1)*var(xdata)/Sxx) } The algorithm converges very quickly. The MLEs are X = 18.24, ^ Y = 2.37, ^ X = .969, ^2 Y = .831, ^2 = -0.969. ^ ...
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