1. Consider a random sample of size n from a population with pdf: f (x) =...
Let X1, ..., Xn be a random sample from a population with pdf f(x 1/8,0 < x < θ, zero elsewhere. Let Yi < < Y, be the order statistics. Show that Y/Yn and Yn are independent random variables
A random sample of size n = 2 is taken from the p.d.f f(x) = {1 for 0 ≤ x ≤ 1 and 0 otherwise. Find P(X-bar ≥ 0.9) 3. A random sample of size n = 2 is taken from the p.d.f 1 for 0 < x < 1 f(30 0 otherwise. Find P(X > 0.9)
4. Let X1,..., X, be a random sample from a population with pdf 0 otherwise Let Xo) <...Xn)be the order statistics. Show that Xu/Xu) and X(n) are independent random variables
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)
IV. Let X be a random variable with the following pdf: f() = (a + 1)2 for 0<< 1 0 elsewhere Find the maximum likelihood estimator of a, based on a random sample of size n. Check if the Maximum Likelihood Estimator in Part (a) is unbiased
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=
X1, X2, ..., Xn constitute a random sample from a population with pdf 2 +0.03) |2|<1 f(0) = 0 {ila. 0.W. where 101 < 1. Determine if X is an unbiased estimator of 8. If not, modify it to make it unbiased, and determine if it is consistent. Justify.
Problem 5 Let Y1 denote the minimum of a random sample of size n from a distribution that has pdf f(x) e(,0x< o0, zero elsewhere X- n (Y1 0), find the cumulative distribution function (cdf) for Zn = n (Y1 - 0), and Let Zn find the limiting cdf of Zn as n >oo.
1. Consider a random sample of size n from a population with probability density function: х fx(x,0) = e 02 exig for x >0,0 >0. (a) Find the Cramer-Rao lower bound for the variance of an unbiased estimator of (b) Find the methods of moment estimator for @ and verifies that it attains the lower bound
(x-2) 5. a) Let S Prove that s? Po? n-1 b) Consider a sequence of random variables {Xn} with pdf, fx, (x) = xht where 1<x<. Obtain Fx (2) and hence find the limiting distribution of X, as noo. c) Consider a random sample of size n from Fx (x) = where - <I<0. Find the limiting distribution of Yn as n + if (a)' = n max{X1, X2, X3,...,xn). and X(n) [17 marks]