1. Let X1, ..., Xn be iid with PDF 1 xle f(x;0) = x>0 (a) Determine...
1 Let X1,..., Xn be iid with PDF x/e f(x;0) ',X>0 o (a) Find the method of moments estimator of e. (b) Find the maximum likelihood estimator of O (c) Is the maximum likelihood estimator of efficient?
Suppose X1, X2, .., Xn is an iid sample from where >0. (a) Derive the size α likelihood ratio test (LRT) for Ho : θ-Bo versus H : θ θο. Derive the power function of the LRT (b) Suppose that n 10, Derive the most powerful (MP) level α-0.10 test of Ho : θ 1 versus Hi: 0-2. Calculate the power of your test
Suppose that X1,X2, ,Xn are iid N(μ, σ2), where both parameters are unknown. Derive the likelihood ratio test (LRT) of Ho : σ2 < σ1 versus Ho : σ2 > σ.. (a) Argue that a LRT will reject Ho when w(x)S2 2 0 is large and find the critical value to confer a size α test. (b) Derive the power function of the LRT
Let (X1, ... , Xn) be an iid sample from the exponentially distributed X with pdf given by f(x;0) = -e ô, x > 0, 0 >0. Use the Neyman-Pearson Lemma to find the a-level most powerful test (MPT) of Ho : 0 = 2 vs H : 0 = 3.
Suppose that X1, X2,..., Xn are iid from where a 1 is a known constant and θ > 0 is an unknown parameter. (a) Show that the likelihood ratio rejection region for testing Ho : θ θο versus H : θ > θο can be written in terms of X(n), the maximum order statistic. (b) Derive the power function of the test in part (a). (c) Derive the most powerful (MP) level α test of Ho : θ-5 versus H1...
6.4.3. Let X1, X2, ..., Xn be iid, each with the distribution having pdf f(x; 01, 02) = (1/02)e-(2–01)/02, 01 < x <ao, -20 < 02 < 0o, zero elsewhere. Find the maximum likelihood estimators of 01 and 02.
Question 2 Let X1,...,X, be iid Geometric random variables with parameter and probability mass function f(T; 7) = (1 - 7)" for 1 = 0,1,2,... and 0 <I<1. We wish to test: HT=0.50 HT70.50 (a) Find the three asymptotic x1) test statistics (Likelihood Ratio, Wald, and Score) for this setting. versus
Consider a random sample X1, ..., Xn from a normal distribution with known mean 0 and unknown variance 0 = 02 (a) Write the likelihood and log-likelihood function (b) Derive the maximum likelihood estimator for 6 (c) Show that the Fisher information matrix is I(O) = 2014 (d) What is the variance of the maximum likelihood estimator for @? Does it attain the Cramer-Rao lower bound? (e) Suppose that you are testing 0 = 1 versus the alternative 0 #...
Let X1,…, Xn be a sample of iid random variable with pdf f (x; ?) = 1/(2x−?+1) on S = {?, ? + 1, ? + 2,…} with Θ = ℕ. Determine a) a sufficient statistic for ?. b) F(1)(x). c) f(1)(x). d) E[X(1)].
Suppose that X1, X2, ..., Xn are independent random variables (not iid) with densities ÍXi(z10,) -.2 e _ θ:/z1(z > 0), where θί 〉 0, for i = 1, 2, , n. (a) Derive the form of the likelihood ratio test (LRT) statistic for testing versuS H1: not Ho. You do not have to find the distribution of the likelihood ratio test (LRT) statistic under Ho- Just find the form of the statistic. (b) From your result in part (a),...