or 6 blem 5(*): Let X be a random variable with pdf 0 otherwise. Compute E(X)....
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).
Question 5 15 marks] Let X be a random variable with pdf -{ fx(z) = - 0<r<1 (1) 0 :otherwise, Xa, n>2, be iid. random variables with pdf where 0> 0. Let X. X2.... given by (1) (a) Let Ylog X, where X has pdf given by (1). Show that the pdf of Y is Be- otherwise, (b) Show that the log-likelihood given the X, is = n log0+ (0- 1)log X (0 X) Hence show that the maximum likelihood...
Question 5 15 marks] Let X be a random variable with pdf -{ fx(z) = - 0<r<1 (1) 0 :otherwise, Xa, n>2, be iid. random variables with pdf where 0> 0. Let X. X2.... given by (1) (a) Let Ylog X, where X has pdf given by (1). Show that the pdf of Y is Be- otherwise, (b) Show that the log-likelihood given the X, is = n log0+ (0- 1)log X (0 X) Hence show that the maximum likelihood...
3. Let X1,... ,Xn 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).
3. Let X1,... ,Xn 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).
4. Let X be continuous random variable whose PDF is given by for r>1 otherwise, where 0 is an unknown scalar parmeter. (a) Find the maximum likelihood estimator of . (b) Find a method-of-moments estimator of θ for the case when θ > 1. (c) Why can we not find a method-of-moments estimator when θ < 1? 151 151 151
(1) Suppose the pdf of a random variable X is 0, otherwise. (a) Find P(2 < X < 3). (b) Find P(X < 1). (e) Find t such that P(X <t) = (d) After the value of X has been observed, let y be the integer closest to X. Find the PMF of the random variable y U (2) Suppose for constants n E R and c > 0, we have the function cr" ifa > 1 0, otherwise (a)...
Let X1 Xn be a random sample from a distribution with the pdf f(x(9) = θ(1 +0)-r(0-1) (1-2), 0 < x < 1, θ > 0. the estimator T-4 is a method of moments estimator for θ. It can be shown that the asymptotic distribution of T is Normal with ETT θ and Var(T) 0042)2 Apply the integral transform method (provide an equation that should be solved to obtain random observations from the distribution) to generate a sam ple of...
Let Xi,... , Xn be a random sample from a normal random variable X with E(X) 0 and var(X)-0, i.e., X ~N(0,0) (a) What is the pdf of X? (b) Find the likelihood function, L(0), and the log-likelihood function, e(0) (c) Find the maximun likelihood estimator of θ, θ (d) Is θ unbiased?
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