Parameter Estimation from Censored Samples using the Expectation-Maximization Algorithm

Details Parameter Estimation from Censored Samples using the Expectation-Maximization Algorithm Parameter Estimation from Censored Samples using the Expectation-Maximization Algorithm

2012-03-17

arxiv.org/abs/1203.3880v1

Statistics

Computation

Scientific paper

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.

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