Efficient and portable combined random number generators
Communications of the ACM
On bootstrapping the number of components in finite mixtures of Poisson distributions
Statistics and Computing
Editorial: recent developments in mixture models
Computational Statistics & Data Analysis
Pattern Recognition Letters
A quick procedure for model selection in the case of mixture of normal densities
Computational Statistics & Data Analysis
A bayesian mixture model with linear regression mixing proportions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic tag recommendation algorithms for social recommender systems
ACM Transactions on the Web (TWEB)
Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection
Computational Statistics & Data Analysis
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Finite mixture models arise in a natural way in that they are modeling unobserved population heterogeneity. An application in disease mapping shows that mixture models are useful in separating signal from noise. Thus, the number of components k of the mixture model needs to be estimated where k = 1 is the important homogenous case. Because of the irregularity of the parameter space, the log-likelihood-ratio statistic (LRS) does not have a χ2 limit distribution and therefore it is difficult to use the LRS to test for the number of components. An alternative approach applies the nonparametric bootstrap such that a mixture algorithm is applied B times to bootstrap samples obtained from the original sample with replacement. The number of components k is obtained as the mode of the bootstrap distribution of k. This approach provides on empirical grounds a mode-unbiased and consistent estimator for the number of components in the homogeneous Poisson case. The distribution of the log-likelihood-ratio statistic (LRS) for the testing problem H0 : k = 1 vs. H1 : k 1 is addressed for the Poisson case. For a very large sample size of n = 10000 this distribution approximates a χ12 distribution.