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Testing for a Poisson Mixture Comparison of the Power of the - dspace sunyconnect suny 2025

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The dissertation by Donghyung Lee, titled 'Testing for a Poisson Mixture: Comparison of the Power of the Posterior Predictive Check (PPC) and Bootstrap Approaches,' explores statistical methods for identifying the number of components in finite mixture models, specifically focusing on Poisson mixtures. It compares the effectiveness of two inferential methods—the bootstrap method and the posterior predictive check (PPC)—in assessing the likelihood ratio test (LRT) statistic. Through simulation studies, the research investigates how these methods perform under different hypotheses, including testing a single Poisson distribution against a two-component Poisson mixture and a zero-inflated Poisson distribution. The findings indicate that both methods yield comparable power and type I error rates, suggesting that PPC can be a viable alternative to bootstrap methods in this context.

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Testing for a Poisson Mixture Comparison of the Power of the - dspace sunyconnect suny 2025

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