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Robust Clustering With Subpopulation-Specific Deviations
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DOI：10.1080/01621459.2019.1611583.png)
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摘要
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The National Birth Defects Prevention Study (NBDPS) is a case-control study of birth defects conducted across 10 U.S. states. Researchers are interested in characterizing the etiologic role of maternal diet, collected using a food frequency questionnaire. Because diet is multidimensional, dimension reduction methods such as cluster analysis are often used to summarize dietary patterns. In a large, heterogeneous population, traditional clustering methods, such as latent class analysis, used to estimate dietary patterns can produce a large number of clusters due to a variety of factors, including study size and regional diversity. These factors result in a loss of interpretability of patterns that may differ due to minor consumption changes. Based on adaptation of the local partition process, we propose a new method, robust profile clustering, to handle these data complexities. Here, participants may be clustered at two levels: (1) globally, where women are assigned to an overall population-level cluster via an overfitted finite mixture model, and (2) locally, where regional variations in diet are accommodated via a beta-Bernoulli process dependent on subpopulation differences. We use our method to analyze the NBDPS data, deriving prepregnancy dietary patterns for women in the NBDPS while accounting for regional variability. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
Keyword:
Food frequency questionnaire
Latent class analysis
Local partition process
Nutritional epidemiology
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Markov chain Monte Carlo estimation of classical and dynamic switching and mixture models经典和动态切换与混合模型的马尔可夫链蒙特卡罗估计
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分享收藏Latent Class Analysis Is Useful to Classify Pregnant Women into Dietary Patterns.png)
JOURNAL OF NUTRITIONOAAI
JOURNAL OF NUTRITIONIF3.8
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