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This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data.
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data.
As the size of data sets grows ever larger, the need for valid statistical tools is greater than ever. This book introduces super learning and the targeted maximum likelihood estimator, and discusses complex data structures and related applied topics.
A fundamental statistical framework for the analysis of complex longitudinal data is provided in this book. The techniques go beyond standard statistical approaches and can be used to teach masters and Ph.D. The text is ideally suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data.
This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research.
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