Penalized likelihood‐ratio test for finite mixture models with multinomial observations
Penalized likelihood‐ratio test for finite mixture models with multinomial observations
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DOI：
10.2307/3315719
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发表时间：
1998-12
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影响因子：
--
通讯作者：
Jiahua Chen
中科院分区：
文献类型：
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作者：
Jiahua Chen
由于有限混合模型的不规则性，常用的似然比统计量通常具有复杂的极限分布。我们建议在对数似然函数中添加一种特殊类型的惩罚函数。当应用于具有多项观测值的有限混合模型时，所得的惩罚似然比统计量具有简单的极限分布。该方法在解决Besloff和Lander（1995）讨论的问题时特别有效。所开发的理论和进行的模拟表明，惩罚似然方法可以给出非常好的结果，例如，比众所周知的C（α）过程更好。然而，本文没有充分探讨惩罚函数和权重的选择。新程序的全部潜力将在今后加以探讨。
Due to the irregularity of finite mixture models, the commonly used likelihood‐ratio statistics often have complicated limiting distributions. We propose to add a particular type of penalty function to the log‐likelihood function. The resulting penalized likelihood‐ratio statistics have simple limiting distributions when applied to finite mixture models with multinomial observations. The method is especially effective in addressing the problems discussed by Chernoff and Lander (1995). The theory developed and simulations conducted show that the penalized likelihood method can give very good results, better than the well‐known C(α) procedure, for example. The paper does not, however, fully explore the choice of penalty function and weight. The full potential of the new procedure is to be explored in the future.