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Handbook of Missing Data Methodology

Handbook of Missing Data Methodology

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research. Divided into six parts the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing including likelihood and Bayesian methods; semi-parametric methods with particular emphasis on inverse probability weighting; and multiple imputation methods. The next part of the book focuses on a range of approaches that assess the sensitivity of inferences to alternative routinely non-verifiable assumptions about the missing data process. The final part discusses special topics such as missing data in clinical trials and sample surveys as well as approaches to model diagnostics in the missing data setting. In each part an introduction provides useful background material and an overview to set the stage for subsequent chapters. Covering both established and emerging methodologies for missing data this book sets the scene for future research. It provides the framework for readers to delve into research and practical applications of missing data methods.

GBP 56.99
1

Missing Data Analysis in Practice

Missing Data Analysis in Practice

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching teaching and consulting in quantitative areas the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches the underlying assumptions and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts with the data sets and codes available online. The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems such as measurement error finite population inference disclosure limitation combing information from multiple data sources and causal inference.

GBP 44.99
1

Applied Missing Data Analysis

Applied Missing Data Analysis

The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions newer model-based imputation strategies and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even three-pronged approach to maximum likelihood estimation (MLE) Bayesian estimation as an alternative to MLE and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking employee turnover and chronic pain. The companion website (www. appliedmissingdata. com) includes data sets and analysis examples from the book up-to-date software information and other resources. New to This Edition *Expanded coverage of Bayesian estimation including a new chapter on incomplete categorical variables. *New chapters on factored regressions model-based imputation strategies multilevel missing data-handling methods missing not at random analyses and other timely topics. *Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples. *Most of the book is entirely new. | Applied Missing Data Analysis

GBP 62.99
1

Flexible Imputation of Missing Data Second Edition

Flexible Imputation of Missing Data Second Edition

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. | Flexible Imputation of Missing Data Second Edition

GBP 38.99
1

Bayesian Nonparametrics for Causal Inference and Missing Data

Bayesian Nonparametrics for Causal Inference and Missing Data

Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data survey data cohort studies and randomized clinical trials. Features • Thorough discussion of both BNP and its interplay with causal inference and missing data • How to use BNP and g-computation for causal inference and non-ignorable missingness • How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions • Detailed case studies illustrating the application of BNP methods to causal inference and missing data • R code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.

GBP 90.00
1