Multiple Imputation and its Application

James R. Carpenter , Jonathan W. Bartlett , Tim P. Morris , Angela M. Wood , Matteo Quartagno 지음

Wiley 2023년 07월 11일 출간

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책소개

Multiple Imputation and its Application The most up-to-date edition of a bestselling guide to analyzing partially observed data In this comprehensively revised Second Edition of Multiple Imputation and its Application, a team of distinguished statisticians delivers an overview of the issues raised by missing data, the rationale for multiple imputation as a solution, and the practicalities of applying it in a multitude of settings. With an accessible and carefully structured presentation aimed at quantitative researchers, Multiple Imputation and its Application is illustrated with a range of examples and offers key mathematical details. The book includes a wide range of theoretical and computer-based exercises, tested in the classroom, which are especially useful for users of R or Stata. Readers will find: A comprehensive overview of one of the most effective and popular methodologies for dealing with incomplete data sets Careful discussion of key concepts A range of examples illustrating the key ideas Practical advice on using multiple imputation Exercises and examples designed for use in the classroom and/or private study Written for applied researchers looking to use multiple imputation with confidence, and for methods researchers seeking an accessible overview of the topic, Multiple Imputation and its Application will also earn a place in the libraries of graduate students undertaking quantitative analyses.

목차

Preface Data acknowledgments Glossary I Foundations 1 1 Introduction 2 1.1 Reasons for missing data . . . . . . . . . . . . . . . . . . . . . 5 1.2 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Patterns of missing data . . . . . . . . . . . . . . . . . . . . . 8 1.3.1 Consequences of missing data . . . . . . . . . . . . . . . 10 1.4 Inferential framework and notation . . . . . . . . . . . . . . . . 13 1.4.1 Missing Completely At Random (MCAR) . . . . . . . . 15 1.4.2 Missing At Random (MAR) . . . . . . . . . . . . . . . . 16 1.4.3 Missing Not At Random (MNAR) . . . . . . . . . . . . 22 1.4.4 Ignorability . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.5 Using observed data to inform assumptions about the missingness mechanism . .. . . . . . . 28 1.6 Implications of missing data mechanisms for regression analyses 32 1.6.1 Partially observed response . . . . . . . . . . . . . . . . 33 1.6.2 Missing covariates . . . . . . . . . . . . . . . . . . . . . 37 1.6.3 Missing covariates and response . . . . . . . . . . . . . . 40 1.6.4 Subtle issues I: the odds ratio . . . . . . . . . . . . . . . 40 1.6.5 Implication for linear regression . . . . . . . . . . . . . . 43 1.6.6 Subtle issues II: sub sample ignorability . . . . . . . . . 44 1.6.7 Summary: when restricting to complete records is valid 45 1.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 1.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2 The Multiple Imputation Procedure and Its Justification 52 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 2.2 Intuitive outline of the MI procedure . . . . . . . . . . . . . . 54 2.3 The generic MI Procedure . . . . . . . . . . . . . . . . . . .

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작가정보

저자(글) James R. Carpenter

저자(글) Jonathan W. Bartlett

저자(글) Tim P. Morris

저자(글) Angela M. Wood

저자(글) Matteo Quartagno