Parameter Estimation from Censored Samples using the Expectation-Maximization Algorithm
Computation
2012-03-20 v1 Other Statistics
Abstract
This paper deals with parameter estimation when the data are randomly right censored. The maximum likelihood estimates from censored samples are obtained by using the expectation-maximization (EM) and Monte Carlo EM (MCEM) algorithms. We introduce the concept of the EM and MCEM algorithms and develop parameter estimation methods for a variety of distributions such as normal, Laplace and Rayleigh distributions. These proposed methods are illustrated with three examples.
Cite
@article{arxiv.1203.3880,
title = {Parameter Estimation from Censored Samples using the Expectation-Maximization Algorithm},
author = {Chanseok Park and Seong Beom Lee},
journal= {arXiv preprint arXiv:1203.3880},
year = {2012}
}