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Q - 1 ( 4 + 5 + 6 + 7 + 8 + 1 0 + 1 0 ) ( a ) Let x

Q-1(4+5+6+7+8+10+10)(a) Let xiN(i,1) with i=i,i=1,dots,n, estimated
as widehat()i=iwidehat(), where i are known constants and is the parameter with its estimator
widehat()=i=1nixii=1ni2
Show that widehat() and i=1n(xi-widehat()i)2 are independent.
(b) Let xiN(,2),i=1,2, iid. Find the amount of information about and 2 in
x1+x2 and x1-x2.
(c) Let xiBernoulli(p),i=1,dots,n, iid. Consider two estimators of p as
widehat(p)1=Sn,widehat(p)2=S+1n+2
where S=i=1nxi. Compare widehat(p)1 and widehat(p)2 using an appropriate risk function.
(d) Let xiN(1,2),i=1,dots,m, iid, and YjN(2,22),j=1,dots,n, iid, be
two independent random samples with all parameters unknown. Find a complete sufficient
statistic of (1,2,2)'. Compute UMVUEs of (1-2)2 and of 2.
(e) Let x1,dots,xnExp() so that f(x;)=e-x,0x,>0. Consider two
competing estimators of ,widehat()1=-logwidehat() and widehat()2=1x, where
widehat()=1n#{in:xi1},x=1ni=1nxi.
Determine the ARE of widehat()1 vs. widehat()2.[If needed, you may use E(x)=1,Var(x)=12.]
(f) In (d), let 1=2=. Find MLE, widehat(), of the vector =(,2)'. Compute Fisher
information matrix and variance-covariance matrix of widehat().
(g) Let YiN(i,2),i=1,dots,n, with i=0+1xi,0,1inR and xi fixed. Assume 0
and 2 known constants (you can take e.g.0=0,2=1 if you like). Taking 1N(0,2)
as prior, computer the posterior distribution of 1 in as simplified form as possible.
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