Let X ...., X., be a random sample from a geometric distribution with parameter , and...
44 Let X,..., X. be a random sample from Find the Pitman estimator for the location parameter (f) Using the prior density g(0)--e-n,”(θ. find the posterior Bayes estimator (g) Of θ. 44 Let X,..., X. be a random sample from Find the Pitman estimator for the location parameter (f) Using the prior density g(0)--e-n,”(θ. find the posterior Bayes estimator (g) Of θ.
3. Let X be a random variable from a geometric distribution with parameter p (P(X- k p(1-P)"-, } k-1 k-1, 2, ...). Find Emin{X, 100
Problem 8 (10 points). Let X be the random variable with the geometric distribution with parameter 0 <p <1. (1) For any integer n > 0, find P(X >n). (2) Show that for any integers m > 0 and n > 0, P(X n + m X > m) = P(X>n) (This is called memoryless property since this conditional probability does not depend on m. Dobs inta T obabilita ndomlu abonn liaht bulb indofootin W
Let Xi , i = 1, · · · , n be a random sample from Poisson(θ) with pdf f(x|θ) = e −θ θ x x! , x = 0, 1, 2, · · · . (a) Find the posterior distribution for θ when the prior is an exponential distribution with mean 1; (b) Find the Bayesian estimator under the square loss function. (c) Find a 95% credible interval for the parameter θ for the sample x1 = 2, x2...
2. 20 marks] Let z1,., xn be a random sample drawn independently from a one-parameter curved normal distribution which has density -oo < x < 00, θ>0, , riid i.e., X r, and 2,2-1 Γη (e) 3 marks Find the Fisher information Z(0) (f) [3 marks] Is θ2 an MVUE of θ? Justify your answer (g) 3 marks] Assume that T = 1.32 and x-3.76 for a random sample of size n = 100. Find the Wald 95% confidence interval...
Let X be the random variable with the geometric distribution with parameter 0 < p < 1. (1) For any integer n ≥ 0, find P(X > n). (2) Show that for any integers m ≥ 0 and n ≥ 0, P(X > n + m|X > m) = P(X > n) (This is called memoryless property since this conditional probability does not depend on m.)
Question 1: 1a) Let the random variable X have a geometric distribution with parameter p , i.e., P(X = x) = pq??, x=1,2,... i) Show that P(X > m)=q" , where m is a positive integer. (5 points) ii) Show that P(X > m+n X > m) = P(X>n), where m and n are positive integers. (5 points) 1b) Suppose the random variable X takes non-negative integer values, i.e., X is a count random variable. Prove that (6 points) E(X)=...
4. Let X,x, X, be a random sample from a uniform distribution on the interval (0,0) (a) Show that the density function of XnX,X2 Xn is given by 0 otherwise (b) Use (a) to calculate E[X)). Caleulate the bias, B). Find a function of X) that is an unbiased estimator of .
A discrete random variable X follows the geometric distribution with parameter p, written X ∼ Geom(p), if its distribution function is A discrete random variable X follows the geometric distribution with parameter p, written X Geom(p), if its distribution function is 1x(z) = p(1-P)"-1, ze(1, 2, 3, ). The Geometric distribution is used to model the number of flips needed before a coin with probability p of showing Heads actually shows Heads. a) Show that fx(x) is indeed a probability...
Letter f and g only. 44 Let X,..., X. be a random sample from (a) Find a sufficient statistic. (b) Find a maximum-likelihood estimator of θ. (c) Find a method-of-moments estimator of θ. (d) Is there a complete sufficient statistic? If so, find it. (e) Find the UMVUE of 0 if one exists. (f) Find the Pitman estimator for the location parameter θ. (g) Using the prior density g(0)--e-n,๑)(8), find the posterior Bayes estimator Of θ. 44 Let X,..., X....