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- W69164811 abstract "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." @default.
- W69164811 created "2016-06-24" @default.
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- W69164811 date "2022-07-08" @default.
- W69164811 modified "2023-10-04" @default.
- W69164811 title "Multiple Imputation Based on Conditional Quantile Estimation" @default.
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- W69164811 doi "https://doi.org/10.2427/8758" @default.
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