(adapted from Wasserman 9.10 - computation) Let iid X1, X2,... X Uniform(0,0). We showed in class (and can get from question 1) that the maximum likelihood estimate is ở = max{X;} = X(m)- Throughout this problem ê and 0, refer to 6E (a) Find the pdf of ô (b) Generate a data set with n = 50 and 0 = 1. Create a histogram of 0 using the non-parametric bootstrap (done in class and previous homework) (c) With the same data set, Create a histogram of 6 using the parametric bootstrap (p.134 in Wasserman, instead of sampling from the original data, use the original data to get a point estimate for the parameters, and sample data from the distribution using those point estimates)
(adapted from Wasserman 9.10 - computation) Let iid X1, X2,... X Uniform(0,0). We showed in class (and can get from question 1) that the maximum likelihood estimate is ở = max{X;} = X(m)- Throughout this problem ê and 0, refer to 6E (a) Find the pdf of ô (b) Generate a data set with n = 50 and 0 = 1. Create a histogram of 0 using the non-parametric bootstrap (done in class and previous homework) (c) With the same data set, Create a histogram of 6 using the parametric bootstrap (p.134 in Wasserman, instead of sampling from the original data, use the original data to get a point estimate for the parameters, and sample data from the distribution using those point estimates)
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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Transcribed Image Text:4. (adapted from Wasserman 9.10 - computation) Let iid X1, X2,.X~
Uniform(0, 0). We showed in class (and can get from question 1) that the
maximum likelihood estimate is 0 = max{X;}
problem 0 and 0, refer to 6LE
X(n)- Throughout this
(a) Find the pdf of 6
(b) Generate a data set with n = 50 and 0 = 1. Create a histogram
of 0 using the non-parametric bootstrap (done in class and previous
homework)
(c) With the same data set, Create a histogram of 0 using the parametric
bootstrap (p.134 in Wasserman, instead of sampling from the original
data, use the original data to get a point estimate for the parameters,
and sample data from the distribution using those point estimates)
(d) Chmpare the product of the two bootstrap methods with the true
distribution (i.e. graph the curve in (a) over the histograms in (b)
and (c))
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