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On bootstrapping the number of components infinite mixtures of Poisson distributions

机译:自举分量的泊松分布的无限混合

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Finite mixture models arise in a natural way in that they are modeling unobserved population heterogeneity. It is assumed that the population consists of an unknown number k of subpopulations with parameters λ_1,...,λ_k receiving weights p_1,..., p_k. Because of the irregularity of the parameter space, the log-likelihood-ratio statistic (LRS) does not have a χ~2 limit distribution and therefore it is difficult to use the LRS to test for the number of components. These problems are circumvented by using the nonparametric bootstrap such that the 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 is presented using the Times newspaper data and investigated in a simulation study for mixtures of Poisson data.
机译:有限混合模型以一种自然的方式出现,因为它们正在建模未观察到的种群异质性。假设总体由未知数量的k个子种群组成,这些子种群的参数λ_1,...,λ_k接收权重p_1,...,p_k。由于参数空间的不规则性,对数似然比统计量(LRS)没有χ〜2极限分布,因此很难使用LRS来测试组件数。通过使用非参数引导程序可以避免这些问题,以便将混合算法应用B次以从替换后的原始样本获得的引导程序样本。获得分量数k作为k的自举分布的模式。使用《时代》报纸数据介绍了这种方法,并在对泊松数据混合的模拟研究中对其进行了研究。

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