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Let X1, X2, ..., X, be iid random variables with a "Rayleigh" density having the following...
Let X1, X2, ..., X, be iid random variables with a "Rayleigh” density having the following pdf: f(x) = 2x2=+*10, 2 > 0 > 0 V лв a) (3 points) Find a sufficient estimator for using the Factorization Theorem. b) (3 points) Find a method of moments estimator for 0. Small help: E(X1) = c) (7 points) What is the MLE of 02 +0 - 10 ? d) (7 points) For a fact, IX has a Gamman, o) distribution. Using...
Let X1, X2, ..., Xn be iid random variables with a "Rayleigh” density having the following pdf: 22 -12 10 f(x) = e x > 0 > 0 0 пе a) (3 points) Find a sufficient estimator for 0 using the Factorization Theorem. b) (3 points) Find a method of moments estimator for 0. Small help: E(X1) = V c) (7 points) What is the MLE of 02 + 0 – 10 ? d) (7 points) For a fact, 21–1...
PROBLEM 3 Let X1, X2,L , X, be iid observations from a distribution with pdf given by f(xl0)=0x0-, 0<x<1, 0<O<00. a) Find the maximum likelihood estimator of O. b) Find the moment estimator of 0. c) (Extra credit) Compare the mean squared error of the two estimators in (a) and (b). Which one is better? (5 points)
Suppose that X1,X2,. X are iid random variables with pdf ,220 (a) Find the maximum likelihood estimate of the parameter a (b) Find the Fisher Information of X1,X2,.. ., Xn and use it to estimate a 95% confidence interval on the MLE of a (c) Explain how the central limit theorem relates to (b).
Suppose that X1, X2,....Xn is an iid sample of size n from a Pareto pdf of the form 0-1) otherwise, where θ > 0. (a) Find θ the method of moments (MOM) estimator for θ For what values of θ does θ exist? Why? (b) Find θ, the maximum likelihood estimator (MLE) for θ. (c) Show explicitly that the MLE depends on the sufficient statistic for this Pareto family but that the MOM estimator does not
Suppose X1, X2, , Xn is an iid sample from a uniform distribution over (θ, θΗθ!), where (a) Find the method of moments estimator of θ (b) Find the maximum likelihood estimator (MLE) of θ. (c) Is the MLE of θ a consistent estimator of θ? Explain.
2 Let X1, X2, ..., X, be independent continuous random variables from the following distribution: (*) - ar-(-) where I 21 and a > 1 You may use the fact: E[X] = -1 2.1 Show that the maximum likelihood estimator of a is â MLE - Srlos Xi 2.2 Show that the method moment estimator for a is: &mom = 1 2.3 Derive a sufficient statistic for a. What theorem are you using to determine sufficiency?
7.2.10 Suppose that X, .., X, are iid with the Rayleigh distribution, that is the common pdf is where θ(> 0) is the unknown parameter. Find the MLE for θ, is the MLE sufficient for θ?
Suppose that X1, X2, ..., Xn is an iid sample, each with probability p of being distributed as uniform over (-1/2,1/2) and with probability 1 - p of being distributed as uniform over (a) Find the cumulative distribution function (cdf) and the probability density function (pdf) of X1 (b) Find the maximum likelihood estimator (MLE) of p. c) Find another estimator of p using the method of moments (MOM)
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?