Titlebook: Statistical Inference Under Mixture Models; Jiahua Chen Book 2023 Springer Nature Singapore Pte Ltd. 2023 EM-algorithm.EM-test.Hypothesis

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Likelihood Ratio Test for Homogeneity,finite mixture models. For those who opt for the likelihood ratio test, a crucial undertaking involves determining the distribution or the limiting distribution of this test. This task becomes exceedingly challenging when the parameter space for subpopulations is unbounded. One solution to this chal

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-Test for Homogeneity,to a specific class of finite mixture models, although its success remains somewhat constrained. An interesting variation of this test is the EM-test, which may be seen as a derivative of the modified likelihood ratio test. In contrast to comparing the maximum possible modified likelihood values und

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-Test for Higher Order, hypotheses within the finite mixture model. While certain aspects of the results align with those of the modified likelihood ratio test, the unique design of the EM-test enables a clear presentation of the limiting distribution. Importantly, this presentation comes with fewer restrictions on the fi

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-Test for Univariate Finite Gaussian Mixture Models,e-parameter distribution family, the underlying principle of the EM-test is generally applicable. This principle involves comparing the degree of improvement achieved after several EM-iterations starting from a fitted null model. However, this approach does not always yield a clear limiting distribu

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