Showing papers on "Mixture model published in 1979"

Mixture Model Tests Of Hierarchical Clustering Algorithms: The Problem Of Classifying Everybody.

Abstract: Due to the effects of outliers, mixture model tests that require all objects to be classified can severely underestimate the accuracy of hierarchical clustering algorithms. More valid and relevant comparisons between algorithms can be made by calculating accuracy at several levels in the hierarchical tree and considering accuracy as a function of the coverage of the classification. Using this procedure, several algorithms were compared on their ability to resolve ten multivariate normal mixtures. All of the algorithms were significantly more accurate than a random linkage algorithm, and accuracy was inversely related to coverage. Algorithms using correlation as the similarity measure were significantly more accurate than those using Euclidean distance (p < .001). A subset of high accuracy algorithms, including single, average, and centroid linkage using correlation, and Ward's minimum variance technique, was identified.

Experimental designs for mixture systems with multicomponent constraints

Abstract: In an earlier paper it was recommended that an experimental design for the study of a mixture system in which the components had lower and upper limits should consist of a subset of the vertices and centroids of the region defined by the limitson the components. This paper extends this methodology to the situation where linear combinations of two or more components (e.g., liquid content=x3+x4+≦0.35) are subject to lower and upper constraints. The CONSIM algorithm, developed by R. E. Wheeler, is recommended for computing the vertices of the resulting experimental region. Procedures for developing linear and quadratic mixture model designs are discussed. A five-component example which has two multiple-component constraints is included to illustrate the proposed methods of mixture experimentation.

Computer Programs: Estimation for Mixtures of Distributions by Direct Maximization of the Likelihood Function

Abstract: Computer programs are given for a procedure for representing a set of grouped data by 1). a mixture of two normal distributions, and 2). a mixture of two Weibull distributions, using maximum likelihood estimates...