Statistical Methods for Handling Incomplete Data

1. Auflage 2013
ISBN: 978-1-4398-4963-7
Verlag: Taylor & Francis

Buch, Englisch, 223 Seiten, Format (B × H): 161 mm x 241 mm, Gewicht: 521 g

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.

Suitable for graduate students and researchers in statistics, the book presents thorough treatments of:

Statistical theories of likelihood-based inference with missing data
Computational techniques and theories on imputation
Methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching

Assuming prior experience with statistical theory and linear models, the text uses the frequentist framework with less emphasis on Bayesian methods and nonparametric methods. It includes many examples to help readers understand the methodologies. Some of the research ideas introduced can be developed further for specific applications.

Zielgruppe

Graduate students and researchers in statistics and biostatistics.

Autoren/Hrsg.

Kim, Jae Kwang

Shao, Jun

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

Introduction. Likelihood-Based Approach. Computation. Imputation. Propensity Scoring Approach. Nonignorable Missing Data. Longitudinal and Clustered Data. Application to Survey Sampling. Statistical Matching. Bibliography. Index.