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Maximum Likelihood Estimation from Linear Combinations of Discrete Probability Functions
- Source :
- Journal of the American Statistical Association. 68:203-206
- Publication Year :
- 1973
- Publisher :
- Informa UK Limited, 1973.
-
Abstract
- In this article the problem of obtaining the maximum likelihood estimates of the parameters from a special type of linear combination of discrete probability functions is discussed. It is shown that when the sample is completely categorized the estimation problem is no more complicated that that of estimating the parameters of each of the component probability functions separately. When the sample is less than completely classified an iterative procedure must be used to obtain solutions to the likelihood equations and it is shown how the problem reduces to that of the full data case. A discussion of the asymptotic properties of the resulting estimators follows.
- Subjects :
- Statistics and Probability
Mathematical optimization
Restricted maximum likelihood
Estimation theory
Likelihood-ratio test
Expectation–maximization algorithm
Applied mathematics
Statistics, Probability and Uncertainty
M-estimator
Maximum likelihood sequence estimation
Likelihood function
Likelihood principle
Mathematics
Subjects
Details
- ISSN :
- 1537274X and 01621459
- Volume :
- 68
- Database :
- OpenAIRE
- Journal :
- Journal of the American Statistical Association
- Accession number :
- edsair.doi...........42dd78095470860098fc7ed6c394e0f7