Applied Missing Data Analysis by Zhou Xiao Hua (22 results)

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Condition: New. pp. 256.

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Hardback. Condition: New. Applied Missing Data Analysis in the Health Sciences A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features: Multiple data sets that can be replicated using SAS®, Stata®, R, and WinBUGS software packagesNumerous examples of case studies to illustrate real-world scenarios and demonstrate applications of discussed methodologiesDetailed appendices to guide readers through the use of the presented data in various software environments Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.…

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Condition: New. This book provides a modern, hands-on guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics. It acknowledges the limitations of established techniques and provides concrete applications of newly developed methods. Series: Statistics in Practice. Num Pages: 256 pages, illustrations. BIC Classification: MBGR; PBT. Category: (P) Professional & Vocational. Dimension: 241 x 161 x 20. Weight in Grams: 516. . 2014. 1st Edition. Hardcover. . . . . …

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Condition: New. This book provides a modern, hands-on guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics. It acknowledges the limitations of established techniques and provides concrete applications of newly developed methods. Series: Statistics in Practice. Num Pages: 256 pages, illustrations. BIC Classification: MBGR; PBT. Category: (P) Professional & Vocational. Dimension: 241 x 161 x 20. Weight in Grams: 516. . 2014. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. …

Hardback. Condition: New. Applied Missing Data Analysis in the Health Sciences A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features: Multiple data sets that can be replicated using SAS®, Stata®, R, and WinBUGS software packagesNumerous examples of case studies to illustrate real-world scenarios and demonstrate applications of discussed methodologiesDetailed appendices to guide readers through the use of the presented data in various software environments Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.…

Hardcover. Condition: Brand New. 1st edition. 230 pages. 9.00x6.00x1.00 inches. In Stock.

Buch. Condition: Neu. Neuware - Applied Missing Data Analysis in the Health Sciences A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features: - Multiple data sets that can be replicated using SAS®, Stata®, R, and WinBUGS software packages - Numerous examples of case studies to illustrate real-world scenarios and demonstrate applications of discussed methodologies - Detailed appendices to guide readers through the use of the presented data in various software environments Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians.…

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Hardcover. Condition: Brand New. 1st edition. 230 pages. 9.00x6.00x1.00 inches. In Stock. This item is printed on demand.

Hardcover. Condition: new. Hardcover. Applied Missing Data Analysis in the Health Sciences A modern and practical guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics With an emphasis on hands-on applications, Applied Missing Data Analysis in the Health Sciences outlines the various statistical methods for the analysis of missing data. The authors acknowledge the limitations of established techniques and provide newly-developed methods with concrete applications in areas such as causal inference. Organized by types of data, chapter coverage begins with an overall introduction to the existence and limitations of missing data and continues into techniques for missing data inference, including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods. The book subsequently covers cross-sectional, longitudinal, hierarchical, survival data. In addition, Applied Missing Data Analysis in the Health Sciences features: Multiple data sets that can be replicated using SAS, Stata, R, and WinBUGS software packagesNumerous examples of case studies to illustrate real-world scenarios and demonstrate applications of discussed methodologiesDetailed appendices to guide readers through the use of the presented data in various software environments Applied Missing Data Analysis in the Health Sciences is an excellent textbook for upper-undergraduate and graduate-level biostatistics courses as well as an ideal resource for health science researchers and applied statisticians. This book provides a modern, hands-on guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics. It acknowledges the limitations of established techniques and provides concrete applications of newly developed methods. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condition: New. PRINT ON DEMAND pp. 256.