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首页> 外文期刊>Journal of statistical computation and simulation >Rounding non-binary categorical variables following multivariate normal imputation: evaluation of simple methods and implications for practice
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Rounding non-binary categorical variables following multivariate normal imputation: evaluation of simple methods and implications for practice

机译：在多元正态插补后对非二进制类别变量进行四舍五入：简单方法的评估及其实践意义

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摘要

We study bias arising from rounding categorical variables following multivariate normal (MVN) imputation. This task has been well studied for binary variables, but not for more general categorical variables. Three methods that assign imputed values to categories based on fixed reference points are compared using 25 specific scenarios covering variables with k = 3,..., 7 categories, and five distributional shapes, and for each k = 3,..., 7, we examine the distribution of bias arising over 100,000 distributions drawn from a symmetric Dirichlet distribution. We observed, on both empirical and theoretical grounds, that one method (projected-distance-based rounding) is superior to the other two methods, and that the risk of invalid inference with the best method may be too high at sample sizes n ≥ 150 at 50% missingness, n ≥ 250 at 30% missingness and n ≥ 1500 at 10% missingness. Therefore, these methods are generally unsatisfactory for rounding categorical variables (with up to seven categories) following MVN imputation.
机译：我们研究由多元正态（MVN）归因后的四舍五入分类变量引起的偏差。对于二进制变量，已经对该任务进行了很好的研究，但对于更一般的分类变量，则没有进行很好的研究。使用25个特定场景比较了三种基于固定参考点将推算值分配给类别的方法，这些场景涵盖了k = 3，...，7个类别和五个分布形状的变量，每个k = 3，...，7 ，我们研究了从对称Dirichlet分布得出的超过100,000个分布的偏差分布。我们从经验和理论上都观察到，一种方法（基于投影距离的舍入）优于其他两种方法，并且在样本大小n≥150时，用最佳方法进行无效推断的风险可能过高。缺失为50％时，缺失为30％时为n≥250，缺失10％时为n≥1500。因此，这些方法对于在MVN插补之后四舍五入分类变量（最多包含七个类别）通常不令人满意。

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  • 作者

    J.C. Galati; K.A. Seaton; K.J. Lee; J.A. Simpson; J.B. Carlin;

  • 作者单位

    Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3052, Australia,Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC 3086, Australia;

    Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC 3086, Australia;

    Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3052, Australia,Department of Paediatrics, University of Melbourne, Parkville, VIC 3052, Australia;

    Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Population Health, University of Melbourne, Parkville, VIC 3052, Australia;

    Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC 3052, Australia,Department of Paediatrics, University of Melbourne, Parkville, VIC 3052, Australia;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    multiple imputation; bias; rounding; multivariate normal; categorical variable; ordinal variable;

    机译：多重插补偏压;四舍五入;多元正态分类变量有序变量;

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