Define f(x) as follows
θ2 -1<=x<0
1-θ2 0<=x>1
0 otherwise
Let X1, … Xn be iid random variables with density f for some unknown θ (0,1),
Let a be the number of Xi which are negatives and b be the number of Xi which are positive. Total number of samples n = a+b.
Problem 4 Define f(x) as follows θ2 -1<=x<0 1-θ2 0<=x>1 0 otherwise Let X1, … Xn be iid random variables with density f for some unknown θ (0,1), Let a be the number of Xi which are negatives and b be the number of Xi which are positive. Total number of samples n = a+b. Find he Maximum likelihood estimator of θ? Is it asymptotically normal in this sample? Find the asymptotic variance Consider the following hypotheses: H0: X is...
Let X1 , . . . , xn be n iid. random variables with distribution N (θ, θ) for some unknown θ > 0. In the last homework, you have computed the maximum likelihood estimator θ for θ in terms of the sample averages of the linear and quadratic means, i.e. Xn and X,and applied the CLT and delta method to find its asymptotic variance. In this problem, you will compute the asymptotic variance of θ via the Fisher Information....
[20 marks] Let xi, . . . , Xn be a random sample drawn independently from a one-parameter curved normal distribution which has density -oo 〈 x 〈 oo, θ > 0, 2πθ nx, and r2 - enote T-1 Tn (d) [3 marks] Find the maximum likelihood estimator θ2 of. (You do not need to perform the second derivative test.) (e) 3 marks Find the Fisher information T( (f) [3 marks] Is θ2 an MVUE of θ? Justify your answer....
1. Let Xi,..., Xn be a random sample from a distribution with p.d.f. f(x:0)-829-1 , 0 < x < 1. where θ > 0. (a) Find a sufficient statistic Y for θ. (b) Show that the maximum likelihood estimator θ is a function of Y. (c) Determine the Rao-Cramér lower bound for the variance of unbiased estimators 12) Of θ
Let X1, X2, ..., Xn be iid with pdf f(x|θ) = θ*x(θ-1). a) Find the Maximum Likelihood Estimator of θ, and b) show that its variance converges to 0 as n approaches infinity. I have no problem with part a, finding the MLE of θ. However, I'm having some trouble with finding the variance. The professor walked us through part b generally, but I need help with univariate transformation for sigma(-ln(xi)) (see picture below - the professor used Y=sigma(-ln(x)), and...
Exercice 6. Let be (Xi,..., Xn) an iid sample from the Bernoulli distribution with parameter θ, ie. I. What is the Maximum Likelihood estimate θ of θ? 2. Show that the maximum likelihood estimator of θ is unbiased. 3. We're looking to cstimate the variance θ (1-9) of Xi . x being the empirical average 2(1-2). Check that T is not unli ator propose an unbiased estimator of θ(1-0).
Let Xi iid∼ Unif(0, θ) for i = 1, 2, 3 and = 4Y(1) = 2Y(2) θ* = (4/3)Y(3) (a) For each estimator compute the bias? (b) For each estimator compute the variance? (c) Which estimator is best and why?
2. Suppose you decide to randomly generate numbers from X ~ Unif(0, ). Your friend will ask for n numbers and then use this information to guess what value you (secretly) chose for θ. Typically, one might use alLE = max Xi = X, to estimate θ. Your friend, however, has meganumerophobia, and is afraid to say the maximum number in the random sample. Instead, he'll say the second largest number: θ = Xn-1. Determine the bias of this estimator...
Suppose that Xi, X2,., Xn is an iid sample from (1- 0) In 0 0, X(T 0, herwise, where the parameter θ satisfies 0 θ 1. (a) Estimate θ using the method of moments (MOM) and using the method of maximum likelihood. Note: I am not sure if you can get closed form expressions for either estimator, but that is OK. Just write out the equation(s) that would need to be solved (numerically) to
12. Suppose XIX, iid X, P(θ, l), where P(0,1) is the one-parameter Pareto distribution with density f(x)-0/10+1 for l < x < 00, Assume that θ >2, so that the model θ/(0-1)(8-2)2 (a) obtain the MME θι from the first moment equation and the MIE θ2 (b) Obtain the asymptotic distributions of these two estimators. (c) Show that the ML is asymptotically superior to the MME P(0,1) has finite mean θ/(9 -1 ) and variance