The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

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mice: Multivariate Imputation by Chained Equations in R

This article gives a comprehensive introduction into one of the main branches of evolutionary computation – the evolution strategies (ES) the history of which dates back to the 1960s in Germany Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are Basic ES algorithms and design principles for variation and selection operators as well as theoretical issues are presented, and future branches of ES research are discussed

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Evolution strategies –A comprehensive introduction

Basics Introduction The problem of missing data Concepts of MCAR, MAR and MNAR Simple solutions that do not (always) work Multiple imputation in a nutshell Goal of the book What the book does not cover Structure of the book Exercises Multiple imputation Historic overview Incomplete data concepts Why and when multiple imputation works Statistical intervals and tests Evaluation criteria When to use multiple imputation How many imputations? Exercises Univariate missing data How to generate multiple imputations Imputation under the normal linear normal Imputation under non-normal distributions Predictive mean matching Categorical data Other data types Classification and regression trees Multilevel data Non-ignorable methods Exercises Multivariate missing data Missing data pattern Issues in multivariate imputation Monotone data imputation Joint Modeling Fully Conditional Specification FCS and JM Conclusion Exercises Imputation in practice Overview of modeling choices Ignorable or non-ignorable? Model form and predictors Derived variables Algorithmic options Diagnostics Conclusion Exercises Analysis of imputed data What to do with the imputed data? Parameter pooling Statistical tests for multiple imputation Stepwise model selection Conclusion Exercises Case studies Measurement issues Too many columns Sensitivity analysis Correct prevalence estimates from self-reported data Enhancing comparability Exercises Selection issues Correcting for selective drop-out Correcting for non-response Exercises Longitudinal data Long and wide format SE Fireworks Disaster Study Time raster imputation Conclusion Exercises Extensions Conclusion Some dangers, some do's and some don'ts Reporting Other applications Future developments Exercises Appendices: Software R S-Plus Stata SAS SPSS Other software References Author Index Subject Index

Flexible Imputation of Missing Data

The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any knowledge about the process that generated the missing data. Two approaches for imputing multivariate data exist: joint modeling (JM) and fully conditional specification (FCS). JM is based on parametric statistical theory, and leads to imputation procedures whose statistical properties are known. JM is theoretically sound, but the joint model may lack flexibility needed to represent typical data features, potentially leading to bias. FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable. FCS provides tremendous flexibility and is easy to apply, but its statistical properties are difficult to establish. Simulation work shows that FCS behaves very well in ...

Multiple imputation of discrete and continuous data by fully conditional specification

SUMMARY The paper extends earlier work on the so-called skew-normal distribution, a family of distributions including the normal, but with an extra parameter to regulate skewness. The present work introduces a multivariate parametric family such that the marginal densities are scalar skew-normal, and studies its properties, with special emphasis on the bivariate case.

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The multivariate skew-normal distribution

Resumen en: In research about the strength of materials, it is of interest to find probabilistic models which describe the behavior of the lifetime of an item as a...

A parametric regression model for possibly censored lifetime data

As mentioned in Chapter 1, the name majorization appears first in HLP (1959). The idea had appeared earlier (HLP, 1929) although unchristened. Muirhead who dealt with Z n + (i.e. vectors of non-negative integers) already had identified the partial order defined in (1.1) (i.e. majorization). But he, when he needed to refer to it, merely called it “ordering.” Perhaps it took the insight of HLP to recognize that little of Muirhead’s work need necessarily be restricted to integers, but the key ideas including Dalton’s transfer principle were already present in Muirhead’s paper. If there was anything lacking in Muirhead’s development, it was motivation for the novel results he obtained. He did exhibit the arithmetic-geometric mean inequality as an example of his general results, but proofs of that inequality are legion. If that was the only use of his “inequalities of symmetric algebraic functions of n letters”, then they might well remain buried in the Edinburgh proceedings. HLP effectively rescued Muirhead’s work from such potential obscurity. In the present book theorems will be stated in generality comparable to that achieved by HLP and will be ascribed to those authors. Muirhead’s priority will not be repeatedly asserted. HLP restricted attention to IR n + , but the restriction to the positive orthant can and will be often dispensed with. First let us establish the relationship between majorization as defined by HLP (i.e., (1.1)) and averaging as defined by Schur.

Majorization in IRn

Variance estimation with diffuse prior information

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Correction to "Bounds on Expectations of Linear Systematic Statistics Based on Dependent Samples"

Following our careful analysis of the normal conditionals example in the last chapter and our brief mention of the exponential conditionals distribution in Chapter 2, it is natural to seek out more general results regarding distributions whose conditionals are posited to be members of quite general exponential families. Indeed the discussion leading up to Theorem 2.4, suggests that things should work well when conditionals are from exponential families. The key reference for the present chapter is Arnold and Strauss (1991). However, it should be mentioned that results due to Besag (1974) in a stochastic process setting anticipate some of the observations in this chapter.

Barry C. Arnold

Papers

A parametric regression model for possibly censored lifetime data

Majorization in IRn

Variance estimation with diffuse prior information

Correction to "Bounds on Expectations of Linear Systematic Statistics Based on Dependent Samples"

Conditionals in Exponential Families