A Case Study of two Clustering Methods based on Maximum Likelihood
- 1 June 1979
- journal article
- Published by Wiley in Statistica Neerlandica
- Vol. 33 (2) , 81-90
- https://doi.org/10.1111/j.1467-9574.1979.tb00665.x
Abstract
Abstract Two commonly used clustering methods based on maximum likelihood are considered in the context of the classification problem where observations of unknown origin belong to one of two possible populations. The basic assumptions and associated properties of the two methods are contrasted and illustrated by their application to some medical data. Also, the problem of updating an allocation procedure is considered.Keywords
This publication has 25 references indexed in Scilit:
- Consistent Regression Methods for Discriminant Analysis with Incomplete DataJournal of the American Statistical Association, 1978
- Estimating the Linear Discriminant Function from Initial Samples Containing a Small Number of Unclassified ObservationsJournal of the American Statistical Association, 1977
- Updating a Diagnostic System Using Unconfirmed CasesJournal of the Royal Statistical Society Series C: Applied Statistics, 1976
- 389: Separating Mixtures of Normal DistributionsPublished by JSTOR ,1975
- On mle of the parameters of a mixture of two normal distributions when the sample size is smallCommunications in Statistics, 1973
- Clustering Methods Based on Likelihood Ratio CriteriaPublished by JSTOR ,1971
- On Identifying the Population of Origin of Each Observation in a Mixture of Observations from Two Normal PopulationsTechnometrics, 1970
- Percentage Points of a Test for ClustersJournal of the American Statistical Association, 1969
- Estimating the components of a mixture of normal distributionsBiometrika, 1969
- On Some Invariant Criteria for Grouping DataJournal of the American Statistical Association, 1967