通过基于熵的标准对不完整数据进行潜在类分析

Q Mathematics

Statistical Methodology Pub Date : 2016-09-01 DOI:10.1016/j.stamet.2016.04.004

Chantal Larose , Ofer Harel , Katarzyna Kordas , Dipak K. Dey

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引用次数: 28

摘要

潜在类分析是通过概率模型将分类数据分组为类。然后，模型选择标准判断模型与数据的拟合程度。在处理不完整的数据时，当前的方法将输入限制为单个预先指定的类数量。我们试图开发一个基于熵的模型选择标准，不限制输入到一个数量的集群。仿真结果表明，新准则相对于AIC和BIC的现行标准表现良好，而家庭研究应用表明，该准则比AIC和BIC提供了更详细和有用的结果。

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Latent class analysis of incomplete data via an entropy-based criterion

Latent class analysis is used to group categorical data into classes via a probability model. Model selection criteria then judge how well the model fits the data. When addressing incomplete data, the current methodology restricts the imputation to a single, pre-specified number of classes. We seek to develop an entropy-based model selection criterion that does not restrict the imputation to one number of clusters. Simulations show the new criterion performing well against the current standards of AIC and BIC, while a family studies application demonstrates how the criterion provides more detailed and useful results than AIC and BIC.

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来源期刊

Statistical Methodology STATISTICS & PROBABILITY-

CiteScore

0.59

自引率

0.00%

发文量

期刊介绍： Statistical Methodology aims to publish articles of high quality reflecting the varied facets of contemporary statistical theory as well as of significant applications. In addition to helping to stimulate research, the journal intends to bring about interactions among statisticians and scientists in other disciplines broadly interested in statistical methodology. The journal focuses on traditional areas such as statistical inference, multivariate analysis, design of experiments, sampling theory, regression analysis, re-sampling methods, time series, nonparametric statistics, etc., and also gives special emphasis to established as well as emerging applied areas.