Data Science in Practice, 1st Edition

[Farid]

Data Science in Practice | 318 | Tom Alby
2014 | Wiley |

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. It covers traditional techniques for missing data inference--including likelihood-based, weighted GEE, multiple imputation, and Bayesian methods--and applies the methodology to rapidly developing areas of research. The book is ideal for courses on biostatistics at the upper-undergraduate and graduate levels and for health science researchers and applied statisticians.

Contents of Download:
Data Science in Practice Chapman.pdf (42.63 MB)


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