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

A First Course in Order Statistics.

Relations, Bounds and Approximations for Order Statistics.@@@Approximate Distributions of Order Statistics: With Applications to Nonparametric Statistics.

The univariate skew-normal distribution was introduced by Azzalini in 1985 as a natural extension of the classical normal density to accommodate asymmetry. He extensively studied the properties of this distribution and in conjunction with coauthors, extended this class to include the multivariate analog of the skew-normal. Arnold et al. (1993) introduced a more general skew-normal distribution as the marginal distribution of a truncated bivariate normal distribution in whichX was retained only ifY satisfied certain constraints. Using this approach more general univariate and multivariate skewed distributions have been developed. A survey of such models is provided together with discussion of related inference questions.

Skewed multivariate models related to hidden truncation and/or selective reporting

Conditional Specification: Concepts and Theorems.- Exact and Near Compatibility in Distributions with Finite Support Sets.- Distributions with Normal Conditionals.- Conditionals in Exponential Families.- Other Conditionally Specified Families.- Improper and Nonstandard Models.- Characterizations Involving Conditional Moments.- Multivariate Extensions.- Estimation in Conditionally Specified Models.- Marginal and Conditional Specification in General.- Conditional Survival Models.- Applications to Modeling Bivariate Extremes.- Bayesian Analysis Using Conditionally Specified Models.- Conditional Specification Versus Simultaneous Equation Models.- Paella.

Conditional specification of statistical models

1: The Distribution of Order Statistics.- Exercises.- 2: Recurrence Relations and Identities for Order Statistics.- 2.0. Introduction.- 2.1. Relations for single moments.- 2.2. Relations for product moments.- 2.3. Relations for covariances.- 2.4. Results for symmetric populations.- 2.5. Results for normal population.- 2.6. Results for two related populations.- 2.7. Results for exchangeable variates.- Exercises.- 3: Bounds on Expectations of Order Statistics.- 3.0. Introduction.- 3.1. Universal bounds in the i.i.d. case.- 3.2. Variations on the Samuelson-Scott theme.- 3.3. Bounds via maximal dependence.- 3.4. Restricted families of parent distributions.- Exercises.- 4: Approximations to Moments of Order Statistics.- 4.0. Introduction.- 4.1. Uniform order statistics and moments.- 4.2. David and Johnson's approximation.- 4.3. Clark and Williams' approximation.- 4.4. Plackett's approximation.- 4.5. Saw's error analysis.- 4.6. Sugiura's orthogonal inverse expansion.- 4.7. Joshi's modified bounds and approximations.- 4.8. Joshi and Balakrishnan's improved bounds for extremes.- Exercises.- 5: Order Statistics From a Sample Containing a Single Outlier.- 5.0. Introduction.- 5.1. Distributions of order statistics.- 5.2. Relations for single moments.- 5.3. Relations for product moments.- 5.4. Relations for covariances.- 5.5. Results for symmetric outlier model.- 5.6 Results for two related outlier models.- 5.7. Functional behaviour of order statistics.- 5.8. Applications in robustness studies.- Exercises.- 6: Record Values.- 6.0. Introduction.- 6.1. Record values.- 6.2. Bounds on mean record values.- 6.3. Record values in dependent sequences.- Exercises.- References.- Author Index.

Barry C. Arnold

Papers

A First Course in Order Statistics.

Relations, Bounds and Approximations for Order Statistics.@@@Approximate Distributions of Order Statistics: With Applications to Nonparametric Statistics.

Skewed multivariate models related to hidden truncation and/or selective reporting

Conditional specification of statistical models

Relations, Bounds and Approximations for Order Statistics