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

/pdf/evolution-strategies-a-comprehensive-introduction-ciqnmqxb8a.pdf

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.

/pdf/the-multivariate-skew-normal-distribution-265gie4y64.pdf

The multivariate skew-normal distribution

9. Record Values

This article has no abstract.

Keywords:

characterization;
conditional specification;
normal conditionals

Conditionally Specified Models

In this paper we will show how we may, through the use of some extended product expressions for the Gamma function on the characteristic functions of the negative logarithm of the product of particular independent Beta random variables, whose second parameters are rational and follow some amenable rules, obtain in a single shot (i) simple expressions for both the probability density and cumulative distribution functions of this product, as well as, concomitantly (ii) very simple and easy to compute alternative finite form expressions for instances of the Meijer G and Fox H functions. These alternative expressions are based on the expressions for the probability density and cumulative distribution functions of the generalized integer Gamma distribution and are not used or recognized by the available softwares.

/pdf/instances-of-the-meijer-g-and-fox-h-functions-and-of-the-35gs073wfv.pdf

Instances of the Meijer G and Fox H functions and of the distribution of the product of independent beta random variables with finite representations

In this chapter details are given on the packages programmed in MathematicaⓇ, Maxima, and R for the implementation of all the tests addressed in Chap. 5. Functions and modules were developed for several tasks such as the computation of p-values and quantiles for each test and to obtain the computed value of the statistics and corresponding p-values from data stored in a data file. As such, also functions and modules used to read these data files, which may have several different internal structures, were programmed. Details are given on the use of all functions and modules, and comparisons are established among the three packages. The full content of all three packages is available on the book’s supplementary material web site.

MathematicaⓇ, Maxima, and R Packages to Implement the Likelihood Ratio Tests and Compute the Distributions in the Previous Chapter

In this chapter the authors consider three multiple products of independent Beta random variables which are shown to have equivalent representations as the exponential of sums of independent integer Gamma r.v.’s, and as such have finite form representations for their distributions.

Barry C. Arnold

Papers

9. Record Values

Conditionally Specified Models

Instances of the Meijer G and Fox H functions and of the distribution of the product of independent beta random variables with finite representations

MathematicaⓇ, Maxima, and R Packages to Implement the Likelihood Ratio Tests and Compute the Distributions in the Previous Chapter

Multiple Products of Independent Beta Random Variables with Finite Form Representations for Their Distributions