文章基本信息

标题：Multiple Imputation Based on Conditional Quantile Estimation
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作者：Matteo Bottai ; Huiling Zhen
期刊名称：Epidemiology, Biostatistics and Public Health
印刷版ISSN：2282-0930
出版年度：2013
卷号：10
期号：1
DOI：10.2427/8758
语种：English
出版社：PREX
摘要：Multiple imputation is a simulation-based approach for the analysis of data with missing observations. It is widely utilized in many set- tings and preeminent among general approaches when the analytical method does not involve a likelihood function or this is too complex. We consider a multiple imputation method based on the estimation of conditional quantiles of missing observations given the observed data. The method does not require modeling a likelihood and has desirable features that may be useful in some practical settings. It can also be applied to impute dependent, bounded, censored and count data. In a simulation study it shows some advantage over the alternative meth- ods considered in terms of mean squared error across all scenarios except when the data arise from a normal distribution where all meth- ods considered perform equally well. We present an application to the estimation of percentiles of body mass index conditional on physical activity assessed by accelerometers.
关键词：Conditional quantiles; Missing data; Multiple imputation; Quan- tile regression; Smoothing splines