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About this product
- DescriptionDue 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, nigrable 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 nparametric methods. It includes many examples to help readers understand the methodologies. Some of the research ideas introduced can be developed further for specific applications.
- Author(s)Jae Kwang Kim,Jun Shao
- PublisherTaylor & Francis Ltd
- Date of Publication23/07/2013
- Country of PublicationUnited States
- ImprintChapman & Hall/CRC
- Content Note2 black & white illustrations, 31 black & white tables
- Weight521 g
- Width156 mm
- Height235 mm
- Spine18 mm
- Format DetailsUnsewn / adhesive bound
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