Let X1,, X, be a random sample from a uniform distribution on the interval (0, 50), where 0> 0. Thus, the population pdf is |습 if 0
Let X1,, X, be a random sample from a uniform distribution on the interval (0, 50), where 0> 0. Thus, the population pdf is |습 if 0
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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![Let X1,, X, be a random sample from a uniform distribution on the
interval (0, 50], where 0 > 0. Thus, the population pdf is
if 0 < x < 50
fo(x) =
0,
otherwise.
(a) Show that the population mean and variance are: E(X) = (2.5)0 and Vo(X) =
(b) Derive a method of moments estimator of 0 based on X1,..., Xn.
(c) Derive the bias and MSE formulas for your method of moments estimator.
(d) Give the likelihood function, clearly specifying its argument and domain (input
space). Draw a rough sketch of the likelihood function and derive the maximum likelihood
estimator of 0, with clear explanation.
(e) Let V = () max{X1,..., Xn}. Derive the cdf of V, that is, derive Fe(v) = Po[V s
%3D
v] for all -o < v< o0.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F7157b572-b6ea-402e-940f-02951f488872%2F9649b35d-1c74-43ab-a269-886c2b903ccd%2F59i4i3_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Let X1,, X, be a random sample from a uniform distribution on the
interval (0, 50], where 0 > 0. Thus, the population pdf is
if 0 < x < 50
fo(x) =
0,
otherwise.
(a) Show that the population mean and variance are: E(X) = (2.5)0 and Vo(X) =
(b) Derive a method of moments estimator of 0 based on X1,..., Xn.
(c) Derive the bias and MSE formulas for your method of moments estimator.
(d) Give the likelihood function, clearly specifying its argument and domain (input
space). Draw a rough sketch of the likelihood function and derive the maximum likelihood
estimator of 0, with clear explanation.
(e) Let V = () max{X1,..., Xn}. Derive the cdf of V, that is, derive Fe(v) = Po[V s
%3D
v] for all -o < v< o0.
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