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Statistical Methods for Handling Incomplete Data
Jae Kwang Kim
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 matchingAssuming 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.
223 pages, 2 black & white illustrations, 31 black & white tables
| Mediji | Grāmatas Hardcover Book (Grāmata ar cieto muguriņu un vāku) |
| Izlaists | 2013. gada 23. jūlijs |
| ISBN13 | 9781439849637 |
| Izdevēji | Taylor & Francis Inc |
| Lapas | 223 |
| Izmēri | 160 × 239 × 19 mm · 458 g |
| Valoda | Angļu |
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