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首页> 外文期刊>The Annals of applied statistics >VARIABLE SELECTION FOR A CATEGORICAL VARYING-COEFFICIENT MODEL WITH IDENTIFICATIONS FOR DETERMINANTS OF BODY MASS INDEX
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VARIABLE SELECTION FOR A CATEGORICAL VARYING-COEFFICIENT MODEL WITH IDENTIFICATIONS FOR DETERMINANTS OF BODY MASS INDEX

机译:分类变化系数模型的可变选择,具有体重指数的确定性标识

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摘要

Obesity has become one of the major public health issues during the last three decades. A considerable number of determinants have been proposed for body mass index (BMI) by a large range of studies from multiple disciplines. In addition, it is well documented that impacts of these determinants are varying across demographic groups. However, little is known about the relative importance of these potential determinants and the varying impacts of all relatively important determinants. Using the shrinkage estimation technique, we propose a variable selection procedure for the categorical varying-coefficient model. We present a simulation study to exam performance of our method in different scenarios. We further apply the proposed method to examine the impacts of a large number of potential determinants on BMI using data from the 2013 National Health Interview Survey in the United States. By our method, the relevant determinants of BMI are identified through the variable selection procedure; and their varying impacts across demographic groups are quantified through the post-selection estimation.
机译:肥胖在过去三十年中已成为主要的公共卫生问题之一。通过来自多个学科的大量研究,已经为体重指数(BMI)提出了相当数量的决定因素。此外,还有很好的记录,这些决定簇的影响越大,横跨人口统计组织。然而,关于这些潜在的决定因素的相对重要性以及所有相对重要的决定簇的不同影响几乎熟知。使用收缩估计技术,我们提出了一种可变选择过程,用于分类变化系数模型。我们提出了一种模拟研究,以在不同场景中的考试性能。我们进一步应用提出的方法来检查大量潜在决定因素对BMI的影响,使用来自美国2013国家健康面试调查的数据。通过我们的方法,通过变量选择程序识别BMI的相关决定簇;它们通过后选择估计量化人口统计组中的不同影响。

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