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Data/hora: 09/06/2024 09:29:45

Multiple imputation of missing values
Provedor de dados: 31
Autores: Royston,   Patrick.
Following the seminal publications of Rubin about thirty years ago, statisticians have become increasingly aware of the inadequacy of “complete-case” analysis of datasets with missing observations. In medicine, for example, observations may be missing in a sporadic way for different covariates, and a complete-case analysis may omit as many as half of the available cases. Hotdeck imputation was implemented in Stata in 1999 by Mander and Clayton. However, this technique may perform poorly when many rows of data have at least one missing value. This article describes an implementation for Stata of the MICE method of multiple multivariate imputation described by van Buuren, Boshuizen, and Knook (1999). MICE stands for multivariate imputation by chained...
Tipo: Journal Article
Palavras-chave: Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2004
URL: http://purl.umn.edu/116244

Multiple imputation of missing values: update
Provedor de dados: 31
Autores: Royston,   Patrick.
This article describes a substantial update to mvis, which brings it more closely in line with the feature set of S. van Buuren and C. G. M. Oudshoorn’s implementation of the MICE system in R and S-PLUS (for details, see http://www.multiple-imputation.com). To make a clear distinction from mvis, the principal program of the new Stata release is called ice. I will give details of how to use the new features and a practical illustrative example using real data. All the facilities of mvis are retained by ice. Some improvements to micombine for computing estimates from multiply imputed datasets are also described.
Tipo: Journal Article
Palavras-chave: Ice; Mvis; Uvis; Micombine; Mijoin; Misplit; Missing data; Missing at random; Multiple imputation; Multivariate imputation; Regression modeling; Research Methods/ Statistical Methods.
Ano: 2005
URL: http://purl.umn.edu/117511