from above L()=f(x1)*f(x2)*f(x3)*...*f(xn)=(+1)n*(x1*x2*..xn)
Ln(L())=n*Ln(+1)+*ln(xi)
(d/d)*Ln(L()) =n/(1+)+ln(xi)
putting (d/d)*Ln(L()) equal to 0;
n/(1+) =-ln(xi)
=-1-n/(ln(xi))
3. (10 points) Based on a random sample of size n from the pdf below, derive...
Consider a random sample of size n from a distribution with pdf (In O* S(x; 6) = Ox! x = 0, 1, ...;0 > 1 10 otherwise (a) Find a complete sufficient statistic for 8. (b) Find the MLE of O. (c) Find the CRLB for 6 (d) Find the UMVUE of In e. (e) Find the UMVUE of (In )? (1) Find the CRLB for (In 02
Let X, X,, ..., X, denote a random sample of size n from a population with pdf (10) = b exp(@m()).0<x<1 where (<O<0. Derive that the likelihood ratio test of H.:0=1 versus H, :0 #1 in terms of T(x) = ŽI (3)
random sample of size n from the p.d.f. 1.8 On the basis of a (x,θ)-θΧθ-1 , 0 < x < 1, θ E Ω = (0,0)derive the MLE of θ
3. Let Xi,... , X,n be a random sample from a population with pdf 0, otherwise, where θ > 0. a) Find the method of moments estimator of θ. (b) Find the MLE θ of θ (c) Find the pdf of θ in (b).
Consider a random sample of size n from a two-parameter exponential dist EXP(e, n). Recall from Exercise 12 that X 1 ., and X are jointly sufficient for O Because Xi:n is complete and sufficient for η for each fixed value of θ, argue from 104.7 that X, and T X1:n X are stochastically independent. ibution, X, 30. Theor (a) Find the MLE θ of θ. (b) Find the UMVUE of η. (c) Show that the conditional pdf of Xi:n...
Q2: ALL STUDENTS (10 Marks] Let X1, ..., Xn be a random sample from the pdf f(x|0) = 0x-?, O<O<O<0. (a) (3 marks) What is a sufficient statistic for 0? (b) (4 marks) Find the MLE of 0. (c) (3 marks) Find the method of moments estimator of 0.
1. Consider a random sample of size n from a population with pdf: f (x) = (1 -p-p, 0 <p<1, x= 1, 2, ... (a) Show that converges in probability to p. (b) Show that converges in probability to p (1 – p). (c) Find the limiting distribution of
8. Consider a random sample of size n from a distribution with pdf f(x) = 0 else (a) Find the pdf of the smallest order statistic, X(i) b) Find E() and Var(X)) c) Find the pdf of the largest order statistic, X(n)
5. Let Yi,Y2, , Yn be a random sample of size n from the pdf (a) Show that θ = y is an unbiased estimator for θ (b) Show that θ = 1Y is a minimum-variance estimator for θ.
Consider a random sample of size n from the distribution with pdf (In )* f(x; 0) = { 0.c! -, 10, =0,1,... otherwise where 0 > 0. (a) (10 pts) Find a complete sufficient statistic for 0. (b) (10 pts) Using Lehmann-Scheffe theorem, find the UMVUE of Ine. You may need the identity c=