Statistical Methods for Handling Incomplete Data

AvJae Kwang Kim,Jun Shao

Inbunden, Engelska, 2021

1 697 kr

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Beskrivning

Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.Features Uses the mean score equation as a building block for developing the theory for missing data analysis Provides comprehensive coverage of computational techniques for missing data analysis Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data Describes a survey sampling application Updated with a new chapter on Data Integration Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.

Produktinformation

Utgivningsdatum:2021-11-19
Mått:156 x 234 x 30 mm
Vikt:653 g
Format:Inbunden
Språk:Engelska
Antal sidor:380
Upplaga:2
Förlag:Taylor & Francis Ltd
ISBN:9780367280543

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Mer om författaren

Jae Kwang Kim is a LAS dean’s professor in the Department of Statistics at Iowa State University. He is a fellow of American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS). He is the recipient of 2015 Gertude M. Cox award, sponsored by Washington Statistical Society and RTI international. Jun Shao is a professor in the Department of Statistics at University of Wisconsin – Madison. He is a fellow of ASA and IMS, a former president of International Chinese Statistical Association and currently the founding editor of Statistical Theory and Related Fields.

Recensioner i media

"As a general comment, I must say that it is probably one of the most extensive, detailed and complete sources of information on the most up-to-date methods to deal with missing data, from simple imputation methods to more complex analysis techniques that take missingness into account. The book is well organized in 12 chapters that although could be read independently based on the readers needs/interest, it does have a hierarchy that makes sense going from more simple early chapters to more complex subjects later in the book."~David Manteigas, ISCB Book Reviews

Innehållsförteckning

1. Introduction2. Likelihood-based Approach3. Computation4. Imputation5. Multiple Imputation6. Fractional Imputation7. Propensity Scoring Approach8. Nonignorable Missing Data9. Longitudinal and Clustered Data10. Application to Survey Sampling11. Data Integration12. Advanced Topics

Mer från samma författare

Statistics in Survey Sampling

Jae Kwang Kim

Weizhi Meng, Moti Yung, Jun Shao