{X₁, X2,..., Xn} be independent and identically distributed random variables with common pdf f(x) depending on two parameters a and b, a, b>0, where the variance is ab². It is known the following estimated value â³. 6³-320, where â and are the maximum likelihood estimates of a and b, respectively, and the estimated information matrix of (â, b) is (1000 200 15 The estimated variance of the maximum likelihood estimator of the variance is equal to k, using the delta method. Find â and 6 (in terms of k).
{X₁, X2,..., Xn} be independent and identically distributed random variables with common pdf f(x) depending on two parameters a and b, a, b>0, where the variance is ab². It is known the following estimated value â³. 6³-320, where â and are the maximum likelihood estimates of a and b, respectively, and the estimated information matrix of (â, b) is (1000 200 15 The estimated variance of the maximum likelihood estimator of the variance is equal to k, using the delta method. Find â and 6 (in terms of k).
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:{X1,X2, ... ,Xn} be independent and identically distributed random
variables with common pdf f (x) depending on two parameters a and b,
a , b >0, where the variance is ab?.
It is known the following estimated value â³ · 63=320, where â and b are the
maximum likelihood estimates of a and b, respectively, and the estimated
information matrix of (â, b) is
(100
20 ).
15 200
The estimated variance of the maximum likelihood estimator of the variance is
equal to k, using the delta method.
Find â and b (in terms of k).
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