Multiple Imputation Based on Conditional Quantile Estimation
|
| Title | Multiple Imputation Based on Conditional Quantile Estimation |
| Authors | |
| 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. |
| Publisher | PREX |
| Date | 2013-03-21 |
| Source | Epidemiology, Biostatistics and Public Health Vol 10, No 1 (2013) |
| Rights | • The authors warrant that the manuscript (or its essential substance) has not been published in any language or format and has not been submitted elsewhere for print or electronic publication consideration• The authors warrant that the manuscript does not contain any material the publication of which would violate any copyright or other personal or proprietary right of any person or entity• The authors warrant that there aren’t potential conflicts of interest• The authors will obtain and include with the manuscript written permission from any respective copyright owners for the use of any textual, illustrative, or tabular materials that have been previously published or are otherwise copyrighted and owned by third parties.When the article is accepted for publication. The authors, hereby agree to transfer to Prex s.p.a. all rights, including those pertaining to electronic forms and transmissions, under existing copyright laws.© Prex SpA |