Please answer number 3 Let X₁, ...., Xn; i = 1,2,..., n is a random sample from thepopulation with the probability mass function:f (x|0) = 0 (1 - 0)* ; x = 0,1,2,... 0 <0 < 1if it is known that parameter 0 has a prior beta probabilityfunction (3;4) as follows:T(7)h(0) = T(3)r (4)1. determine the likelihood function L(x|0)2. find the density function with X and 0, i.e. g(x|8)3. find the posterior distribution for 0, i.e. k(0|x)4. find the Bayesian estimator for 0, that is, T0² (1-0)³ ; 0 <0 <1

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Answered: 3. find the posterior distribution for…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Consider a random variable, Y , which has a quasi-Bernoulli structure. With probability p ∈ [0, 1] it takes value 0. With probability (1 − p) it is described by a continuous random variable, X , with the following PDF, f_X (x)=1+x, x∈[−1,0), −1≤x<0f_X (x)=1−x, x∈[0,1], 0≤x≤1f_X (x)=0, otherwise , x>1 Obtain CDF of Y, F_Y (y), and draw the sketch.
LetX1,X2,...,Xn be a sequence of independent and identically distributed random variables having the Exponential(λ) distribution,λ >0, fXi(x) ={λe−λx, x >0 0, otherwise (a) Show that the moment generating function mX(s) :=E(esX) =λ/(λ−s) for s< λ;
Let X1, ...., Xn be a random sample from a population with θ unknown and given by the density f(x; θ) = ( 1 2θ √2 x e − √2 x θ if x > 0 0 if x ≤ 0 1. Show that E(X) = 2θ 2 and E( √2 X) = θ (Hint: you may use that R ∞ 0 e −z z α−1dz = (α − 1)! for every α ∈ N). 2. Show that the statistic θbn := 1 n Xn i=1 p2 Xi (1) is an unbiased estimator of θ. 3. Give the definition of a consistent estimator. 4. Show that the estimator θbn given in relation (1) is a consistent estimator of θ. 5. Show that the estimator θbn is a minimum variance estimator of θ. (Hint: use the Cramer-Rao inequality given by var(θb) ≥ 1 nE ∂ ln(f(X;θ) ∂θ 2
Let X1, . . . , Xn i.i.d. U([θ1, θ2]), i.e., X1, . . . , Xn are independent and follow a uniform distribution on the interval [θ1, θ2] for θ1, θ2 ∈ R and θ1 < θ2. Find an estimator for θ1 and θ2 using the method of moments.
2)Let X1, X2, ..., Xn be a sample of n units from a population with a probability density function f (x I θ)=θxθ-1 , 0<x<1, θ>0 . According to this: Find the maximum likelihood estimator (MLE) of parameter θ.
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An insurance company provides customers with both auto and home insurance policies. For a particular customer, Χ is the deduction on his or her auto policy and Y is the deduction on the home policy. Possible values of Χ are K100 and K250, and for Y are K0, K100 and K200. The joint probability density function for (Χ,Y ) is given by the following table: X Y K100 K250 K0 0.20 0.05 K100 0.10 0.15 K200 0.20 0.30 Find the probability for a randomly selected policy holder having a K100 deduction on the auto insurance and a K200 deduction on the home insurance. Find the probability that a randomly selected policy holder has a home deduction of at least K100. Are the random variables Χ and Y independent? Explain your answer. If we look only at those insurance customers selecting the lowest auto mobile insurance deduction (K100), what is the probability that a randomly selected…

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