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3. In genetic investigations, one frequently samples from a binomial distribution. Except that observations of x = 0 are impossible; so in fact, the sampling is from the conditional (truncated) distribution: f(x) = (x) 1-(1-p)m p*(1-p) m-x x/1-(1-p)m (1,2....mm) (x) Find the maximum likelihood estimator for p in the case of m= 2 samples of size n. 3

3. In genetic investigations, one frequently samples from a binomial distribution. Except that observations of x = 0 are impossible; so in fact, the sampling is from the conditional (truncated) distribution: f(x) = (x) 1-(1-p)m p*(1-p) m-x x/1-(1-p)m (1,2....mm) (x) Find the maximum likelihood estimator for p in the case of m= 2 samples of size n. 3

MATLAB: An Introduction with Applications
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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3. In genetic investigations, one frequently samples from a binomial distribution. Except that observations of x = 0 are impossible; so in fact, the sampling is from the conditional (truncated) distribution: px m-x f(x) = (²-1² 1 (1.2) (x() Find the maximum likelihood estimator for p in the case of m = 2 samples of size n. 3
Transcribed Image Text:3. In genetic investigations, one frequently samples from a binomial distribution. Except that observations of x = 0 are impossible; so in fact, the sampling is from the conditional (truncated) distribution: px m-x f(x) = (²-1² 1 (1.2) (x() Find the maximum likelihood estimator for p in the case of m = 2 samples of size n. 3
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