MATLAB code to generate the required distribution:
clc;
clear all;
close all;
u=rand(1, 1000); %generate the 1000 random variables
set=3*u.^(1/3); %set-the required random variable collection
%disp(set); %to display the set variables: here it was
commneted
disp(mean(set)); %display the mean of operation
%here the mean
Here are the set variables:
Given the pdf,f(x) = x2/9 on 0 < x 3, develop a generator for this distribution. Generate 1000 va...
Develop a generator for a random variable whose pdf is F(x) ={ 1/3, 0<=x<=2 1/24, 2<x<=10 0, otherwise a) Write a computer routine to generate 1000 values. b) Plot a histogram of 1000 generated values. c) Perform goodness-of-fit test to determine whether these generated values fits the theoretical density function given above. Note: Invlude your computer routine for generating random variates in your answer sheet. I need numerical solution
Develop a random-variate generator using the inverse-transform technique for a random variable X with the pdf e2o0
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)
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...
2) Consider a random variable with the following probability distribution: P(X-0)-0., Px-1)-0.2, PX-2)-0.3, PX-3) -0.3, and PX-4)-0.1 A. Generate 400 values of this random variable with the given probability distribution using simulation. B. Compare the distribution of simulated values to the given probability distribution. Is the simulated distribution indicative of the given probability distribution? Explain why or why not. C. Compute the mean and standard deviation of the distribution of simulated values. How do these summary measures compare to the...
(+3) The pdf f(x) of a random variable X is given by 0, ifx<0 Find the cumulative distribution function F(r
That is, the distribution of X has pdf given by θ-11(1 < x 0) and a point mass on {x-1). (b) Let X1, X2,..., Xn be a random sample from the distribution in part (a). Show that the pdf of the maximum order statistic X(n) is given by JX(n) (c) Show that X(n) is a sufficient statistic for θ. Is X(n) complete?
1. Given f(x) = kx(9 - x2)4 0<x<3, otherwise a) Find k. f(x) 20 a pdf pro b) Calculate F(x) and the three quartiles. c) Calculate E(X2) and Var(x2). d) Calculate E(X) and Var(x). (needs more than calc 1) (Bonus)
2) Consider a random variable with the following probability distribution: P(X = 0) = 0.1, P(X=1) =0.2, P(X=2) = 0.3, P(X=3) = 0.3, and P(X=4)= 0.1. A. Generate 400 values of this random variable with the given probability distribution using simulation. B. Compare the distribution of simulated values to the given probability distribution. Is the simulated distribution indicative of the given probability distribution? Explain why or why not. C. Compute the mean and standard deviation of the distribution of simulated...
Suppose X, X2,... ,Xn represent a random sample from the pdf f(x;0) 2/02, 0 < x 〈 θ. Note, this pdf does not follow the regularity conditions. Nevertheless, find the MLE θη f . Make sure ElPn or a sample siZe n