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

Statistics for Spatial Data.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Statistical Analysis with Missing Data

Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.

/pdf/a-population-genetic-signal-of-polygenic-adaptation-37rxfmcqye.pdf

A Population Genetic Signal of Polygenic Adaptation

The Simple Linear Regression Model.- The Multiple Linear Regression Model and Its Extensions.- The Generalized Linear Regression Model.- Exact and Stochastic Linear Restrictions.- Prediction in the Generalized Regression Model.- Sensitivity Analysis.- Analysis of Incomplete Data Sets.- Robust Regression.- Models for Categorical Response Variables.

Linear Models and Generalizations: Least Squares and Alternatives

/pdf/introduction-to-statistics-and-data-analysis-4yn4669vv5.pdf

Introduction to Statistics and Data Analysis

http://home.iitk.ac.in/~shalab/paper/94.pdf

Coefficient of determination for multiple measurement error models

The present article discusses the role of categorical variable in the problem
of multicollinearity in linear regression model. It exposes the diagnostic tool
condition number to linear regression models with categorical explanatory variables
and analyzes how the dummy variables and choice of reference category
can affect the degree of multicollinearity. Such an effect is analyzed analytically
as well as numerically through simulation and real data application.

/pdf/role-of-categorical-variables-in-multicollinearity-in-the-4409f77fpc.pdf

Role of Categorical Variables in Multicollinearity in the Linear Regression Model

Journal of the Royal Statistical Society: Series A (Statistics in Society)Volume 185, Issue 2 p. 742-743 BOOK REVIEW Theory of ridge regression estimation with applications , Saleh, A.K. Md. Ehsanes, Arashi, Mohammad, Kibria, B.M. Golam, 2019 John Wiley & Sons, Inc., Hobokon, pp. xxxiv + 342, ISBN 97811186446148 Shalabh, Shalabh shalab@iitk.ac.in Department of Mathematics & Statistics, Indian Institute of Technology Kanpur, Kanpur, IndiaSearch for more papers by this author Shalabh, Shalabh shalab@iitk.ac.in Department of Mathematics & Statistics, Indian Institute of Technology Kanpur, Kanpur, IndiaSearch for more papers by this author First published: 03 February 2022 https://doi.org/10.1111/rssa.12816Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume185, Issue2April 2022Pages 742-743 RelatedInformation 

Theory of ridge regression estimation with applicationsA.K. Md. EhsanesSaleh, MohammadArashi, B.M. GolamKibria, 2019John Wiley & Sons, Inc., Hobokon, pp. xxxiv + 342, ISBN 97811186446148

Shalabh

Papers

Linear Models and Generalizations: Least Squares and Alternatives

Introduction to Statistics and Data Analysis

Coefficient of determination for multiple measurement error models

Role of Categorical Variables in Multicollinearity in the Linear Regression Model

Theory of ridge regression estimation with applicationsA.K. Md. EhsanesSaleh, MohammadArashi, B.M. GolamKibria, 2019John Wiley & Sons, Inc., Hobokon, pp. xxxiv + 342, ISBN 97811186446148