Journal ArticleDOI
Partially parametric techniques for multiple imputation
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This paper compares partially parametric and fully parametric regression-based multiple-imputation methods for handling data sets with missing values and provides an example of how multiple imputation can be used to combine information from two cohorts to estimate quantities that cannot be estimated directly from either one of the cohorts separately.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 1996-08-10. It has received 307 citations till now. The article focuses on the topics: Semiparametric regression & Parametric statistics.read more
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Multiple imputation using chained equations: Issues and guidance for practice
TL;DR: The principles of the method and how to impute categorical and quantitative variables, including skewed variables, are described and shown and the practical analysis of multiply imputed data is described, including model building and model checking.
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Multiple imputation: a primer:
TL;DR: Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
Book
Flexible Imputation of Missing Data
TL;DR: The problem of missing data concepts of MCAR, MAR and MNAR simple solutions that do not (always) work multiple imputation in a nutshell and some dangers, some do's and some don'ts are covered.
Journal ArticleDOI
Multiple imputation of discrete and continuous data by fully conditional specification
TL;DR: FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable, but its statistical properties are difficult to establish.
Journal ArticleDOI
Principled missing data methods for researchers.
Yiran Dong,Chao-Ying Joanne Peng +1 more
TL;DR: Quality of research will be enhanced if researchers explicitly acknowledge missing data problems and the conditions under which they occurred, principled methods are employed to handle missing data, and the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.
References
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Journal ArticleDOI
Classification and Regression Trees.
Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
BookDOI
Density estimation for statistics and data analysis
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Book
Multiple imputation for nonresponse in surveys
TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.
Journal ArticleDOI
Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.