Logistic Regression With Missing Covariate Data

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Logistic Regression with Missing Values in the Covariates

Author : Werner Vach
Publisher : Springer Science & Business Media
ISBN 13 : 1461226503
Total Pages : 152 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Logistic Regression with Missing Values in the Covariates by : Werner Vach

Download or read book Logistic Regression with Missing Values in the Covariates written by Werner Vach and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications. The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.

Logistic Regression with Missing Covariate Data

Author : Marjorie Ireland
Publisher :
ISBN 13 :
Total Pages : 242 pages
Book Rating : 4.:/5 (328 download)

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Book Synopsis Logistic Regression with Missing Covariate Data by : Marjorie Ireland

Download or read book Logistic Regression with Missing Covariate Data written by Marjorie Ireland and published by . This book was released on 1995 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Comparison of Methods for Logistic Regression when a Covariate is Missing

Author : Marcia Dawn Watkins
Publisher :
ISBN 13 :
Total Pages : 216 pages
Book Rating : 4.:/5 (171 download)

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Book Synopsis Comparison of Methods for Logistic Regression when a Covariate is Missing by : Marcia Dawn Watkins

Download or read book Comparison of Methods for Logistic Regression when a Covariate is Missing written by Marcia Dawn Watkins and published by . This book was released on 1986 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Methods for Analyzing Missing Covariate Data

Author : Lan Huang
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)

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Book Synopsis Statistical Methods for Analyzing Missing Covariate Data by : Lan Huang

Download or read book Statistical Methods for Analyzing Missing Covariate Data written by Lan Huang and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing covariate data often arise in various settings, including surveys, clinical trials, epidemiological studies, biological studies and environmental studies. Large scale studies often have large fractions of missing data, which can present serious problems to the data analyst. Motivated by real data applications, this dissertation addresses several aspects in modeling and analyzing data with missing covariates. First, we propose Bayesian methods for estimating parameters in generalized linear models (GLM's) with nonignorably missing covariate data. We specify a parametric distribution for the response variable given the covariates (GLM), a parametric distribution for the missing covariates, and a parametric multinomial selection model for the missing data mechanism. Then we characterize general conditions for the propriety of the joint posterior distribution of the parameters and extend two model selection criteria, weighted L measure and Deviance Information Criterion for model comparison in the presence of missing covariates. Second, we develop a novel modeling strategy for analyzing data with repeated binary responses over time as well as with time-dependent missing covariates. We use the generalized linear mixed logistic model for the repeated binary responses and then propose a joint model for time-dependent missing covariates using information from different sources. The Monte Carlo EM algorithm is developed for computing the maximum likelihood estimates. An extended version of the AIC criterion is proposed to identify factors of interest that may disrupt the cyclical pattern of flowering. Third, we develop an efficient Gibbs sampling algorithm to sample from the joint posterior distribution for the generalized linear mixed logistic model. Moreover, we propose a novel Monte Carlo method to compute a Bayesian model comparison criterion, DIC, for any variable subset model using a single Markov Chain Monte Carlo sample from the full model without sampling from the posterior distribution under each subset model. In the end, we provide a brief discussion of future research.

Model-Free Curve Estimation

Author : Michael E. Tarter
Publisher : CRC Press
ISBN 13 : 9780412042515
Total Pages : 304 pages
Book Rating : 4.0/5 (425 download)

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Book Synopsis Model-Free Curve Estimation by : Michael E. Tarter

Download or read book Model-Free Curve Estimation written by Michael E. Tarter and published by CRC Press. This book was released on 1993-12-16 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model-Free Curve Estimation details the Fourier series approach to density estimation and explores how model-free technology can be expanded to deal with other statistical curves, such as survival and regression functions. It also describes the implementation of some curves for exploratory data analysis, including a specialized curve for detecting and analyzing hidden subpopulations in data and a family of curves useful for finding the best transformation and model to use in a statistical analysis.

Multiple Imputation and its Application

Author : James Carpenter
Publisher : John Wiley & Sons
ISBN 13 : 1119942276
Total Pages : 368 pages
Book Rating : 4.1/5 (199 download)

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Book Synopsis Multiple Imputation and its Application by : James Carpenter

Download or read book Multiple Imputation and its Application written by James Carpenter and published by John Wiley & Sons. This book was released on 2012-12-21 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to analysing partially observeddata. Collecting, analysing and drawing inferences from data iscentral to research in the medical and social sciences.Unfortunately, it is rarely possible to collect all the intendeddata. The literature on inference from the resultingincomplete data is now huge, and continues to grow both asmethods are developed for large and complex data structures, and asincreasing computer power and suitable software enable researchersto apply these methods. This book focuses on a particular statistical method foranalysing and drawing inferences from incomplete data, calledMultiple Imputation (MI). MI is attractive because it is bothpractical and widely applicable. The authors aim is to clarify theissues raised by missing data, describing the rationale for MI, therelationship between the various imputation models and associatedalgorithms and its application to increasingly complex datastructures. Multiple Imputation and its Application: Discusses the issues raised by the analysis of partiallyobserved data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider whenanalysing incomplete data from both observational studies andrandomized trials. Provides a detailed discussion of the practical use of MI withreal-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactionswith multiple imputation, survival analysis, multilevel multipleimputation, sensitivity analysis via multiple imputation, usingnon-response weights with multiple imputation and doubly robustmultiple imputation. Multiple Imputation and its Application is aimed atquantitative researchers and students in the medical and socialsciences with the aim of clarifying the issues raised by theanalysis of incomplete data data, outlining the rationale for MIand describing how to consider and address the issues that arise inits application.

Mixed Effects Models for the Population Approach

Author : Marc Lavielle
Publisher : CRC Press
ISBN 13 : 1482226510
Total Pages : 380 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Mixed Effects Models for the Population Approach by : Marc Lavielle

Download or read book Mixed Effects Models for the Population Approach written by Marc Lavielle and published by CRC Press. This book was released on 2014-07-14 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects ModelsMixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whol

Flexible Imputation of Missing Data, Second Edition

Author : Stef van Buuren
Publisher : CRC Press
ISBN 13 : 0429960352
Total Pages : 444 pages
Book Rating : 4.4/5 (299 download)

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Book Synopsis Flexible Imputation of Missing Data, Second Edition by : Stef van Buuren

Download or read book Flexible Imputation of Missing Data, Second Edition written by Stef van Buuren and published by CRC Press. This book was released on 2018-07-17 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.

Logistic Regression with Incompletely Observed Binary Covariates

Author : Hai-An Hsu
Publisher :
ISBN 13 :
Total Pages : 254 pages
Book Rating : 4.:/5 (341 download)

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Book Synopsis Logistic Regression with Incompletely Observed Binary Covariates by : Hai-An Hsu

Download or read book Logistic Regression with Incompletely Observed Binary Covariates written by Hai-An Hsu and published by . This book was released on 1995 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Logistic regression is one of the most important tools in the analysis of epidemiological and clinical data. Such data often contain missing values for one or more variables. Common practice is to eliminate all individuals for whom any information is missing. This deletion approach does not make efficient use of available information and often introduces bias.

Latent Variable Regression Analysis with Missing Covariates

Author : Qian Li Xue
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783838321578
Total Pages : 148 pages
Book Rating : 4.3/5 (215 download)

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Book Synopsis Latent Variable Regression Analysis with Missing Covariates by : Qian Li Xue

Download or read book Latent Variable Regression Analysis with Missing Covariates written by Qian Li Xue and published by LAP Lambert Academic Publishing. This book was released on 2009-11 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data often arises in regression analysis either by study design or stochastic censoring. Restriction of analysis to complete observations may yield biased inferences. Developing likelihood-based methods for analyzing missing data in a regression setting has largely focused on missing values in the dependent variable. In this book, we discuss two likelihood-based approaches to inference for the regression of multivariate categorical outcomes on a set of covariates when some of the covariate values are missing. Specifically, this research seeks to develop methodologies in the context of latent variable models that (i) synthesize multiple outcomes into an latent construct that is easily interpretable yet retains relevant heterogeneity in individual outcomes; (ii) account for measurement inaccuracy in observable outcomes; (iii) model the association between the latent construct and covariates; (iv) handle missing covariate data in both ignorable and nonignorable cases. This book should be of particular interest to psychosocial scientists and others who plan to use latent variables models, but are discouraged by the daunting analytical difficulties associated with missing data.

Analysis of Incomplete Multivariate Data

Author : J.L. Schafer
Publisher : CRC Press
ISBN 13 : 9781439821862
Total Pages : 478 pages
Book Rating : 4.8/5 (218 download)

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Book Synopsis Analysis of Incomplete Multivariate Data by : J.L. Schafer

Download or read book Analysis of Incomplete Multivariate Data written by J.L. Schafer and published by CRC Press. This book was released on 1997-08-01 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets in which the variables are continuous, categorical, or both. The focus is applied, where necessary, to help readers thoroughly understand the statistical properties of those methods, and the behavior of the accompanying algorithms. All techniques are illustrated with real data examples, with extended discussion and practical advice. All of the algorithms described in this book have been implemented by the author for general use in the statistical languages S and S Plus. The software is available free of charge on the Internet.

The EM Algorithm and Extensions

Author : Geoffrey J. McLachlan
Publisher : John Wiley & Sons
ISBN 13 : 0470191600
Total Pages : 399 pages
Book Rating : 4.4/5 (71 download)

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Book Synopsis The EM Algorithm and Extensions by : Geoffrey J. McLachlan

Download or read book The EM Algorithm and Extensions written by Geoffrey J. McLachlan and published by John Wiley & Sons. This book was released on 2007-11-09 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.

Missing Covariates in Logistic Regression, Estimation and Distribution Selection

Author : Fabrizio Consentino
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (837 download)

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Book Synopsis Missing Covariates in Logistic Regression, Estimation and Distribution Selection by : Fabrizio Consentino

Download or read book Missing Covariates in Logistic Regression, Estimation and Distribution Selection written by Fabrizio Consentino and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Logistic Regression Models

Author : Joseph M. Hilbe
Publisher : CRC Press
ISBN 13 : 1420075772
Total Pages : 658 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Logistic Regression Models by : Joseph M. Hilbe

Download or read book Logistic Regression Models written by Joseph M. Hilbe and published by CRC Press. This book was released on 2009-05-11 with total page 658 pages. Available in PDF, EPUB and Kindle. Book excerpt: Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models t

Logistic Regression when Covariates are Missing at Random

Author : Te-Ching Chen
Publisher :
ISBN 13 :
Total Pages : 86 pages
Book Rating : 4.:/5 (48 download)

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Book Synopsis Logistic Regression when Covariates are Missing at Random by : Te-Ching Chen

Download or read book Logistic Regression when Covariates are Missing at Random written by Te-Ching Chen and published by . This book was released on 1998 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Analysis with Missing Data

Author : Roderick J. A. Little
Publisher : John Wiley & Sons
ISBN 13 : 1118595696
Total Pages : 463 pages
Book Rating : 4.1/5 (185 download)

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Book Synopsis Statistical Analysis with Missing Data by : Roderick J. A. Little

Download or read book Statistical Analysis with Missing Data written by Roderick J. A. Little and published by John Wiley & Sons. This book was released on 2019-03-21 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

Examining the Effects of Class Imbalance with Missing Data on the Logistic Regression Model

Author : Joel L'Artisan
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

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Book Synopsis Examining the Effects of Class Imbalance with Missing Data on the Logistic Regression Model by : Joel L'Artisan

Download or read book Examining the Effects of Class Imbalance with Missing Data on the Logistic Regression Model written by Joel L'Artisan and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of class imbalance has become frequent, and missing data also are often a predicament in the social sciences. In the literature, the two problems have been debated substantially and separately. Class imbalance has been shown to adversely impact the estimation of logistic regression coefficients. Also, the missing data problem has been demonstrated to yield bias in parameter estimation in various types of statistical analyses. This simulation study focuses on the context logistic regression and investigates how class imbalance interact with missing data to impact the estimation of the coefficients and test procedures such as the Wald and likelihood ratio tests. Three factors are considered in this simulation-(1) missing data mechanisms MCAR, MAR, MNAR, (2) sample sizes-small, 100; medium, 500; and large, 1000, (3) class imbalance ratios 10%, 20%, 30%, 40%, and 50%. The results demonstrate that the combination of class imbalance and missing data problems affects the performance of the logistic regression, particularly when the imbalance ratio is as low as 10% and the sample size is also low, and potential methods for the remedies are suggested.