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Statistical Methods for Handling Incomplete Data

Jae Kwang Kim | Jun Shao
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A01=Jae Kwang Kim
A01=Jun Shao
advanced statistical modeling
analyzing incomplete data
Author_Jae Kwang Kim
Author_Jun Shao
Bart
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Category=PS
computational statistics
computational techniques for missing data analysis
Conditional Expectation
Em Algorithm
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fractional imputation
GMM
graduate statistics textbook
Imputation Estimator
KRR
likelihood-based inference with missing data
longitudinal missing data
Maximum Entropy Estimator
Maximum Entropy Method
mean score equation and missing data analysis
Missing Data Analysis
missing data techniques for researchers
Natural Cubic Splines
neural network imputation
nonignorable missing data
Nonignorable Nonresponse
Nonparametric Imputation
Nonparametric Maximum Likelihood Estimators
Observed Likelihood Function
Propensity Score
Propensity Score Weighting
quantitative data analysis
Regression Model
Representer Theorem
Reproducing Kernel Hilbert Space Theory
RKHS
Save
Scad Method
Scad Penalty
SDR Method
Sir
statistical matching
survey methodology
survey sampling

Product details

  • ISBN 9781032118130
  • Weight: 760g
  • Dimensions: 156 x 234mm
  • Publication Date: 29 Jan 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

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.
About Jae Kwang Kim | Jun Shao

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.

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