Fitting Mixture Models to Grouped and Truncated Data via the EM Algorithm
- 1 June 1988
- journal article
- research article
- Published by JSTOR in Biometrics
- Vol. 44 (2) , 571-578
- https://doi.org/10.2307/2531869
Abstract
The fitting of finite mixture models via the EM algorithm is considered for data which are available only in grouped form and which may also be truncated. A practical example is presented where a mixture of two doubly truncated log-normal distributions is adopted to model the distribution of the volume of red blood cells in cows during recovery from anemia.This publication has 2 references indexed in Scilit:
- On Bootstrapping the Likelihood Ratio Test Stastistic for the Number of Components in a Normal MixtureJournal of the Royal Statistical Society Series C: Applied Statistics, 1987
- Analysis of the Volume of Red Blood Cells: Application of the Expectation- Maximization Algorithm to Grouped Data from the Doubly-Truncated Lognormal DistributionBiometrics, 1986