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Explore the foundations of, and cutting-edge developments in, statistics
Statistical Planning and Inference: Concepts and Applications delivers a robust introduction to statistical planning and inference, including classical and computer age developments in statistical science. The book examines the challenges faced in statistical planning and inference, exploring the optimum methods identifying limitations and commonly encountered pitfalls.
It addresses linear and non-linear statistical inference and discusses noise-effect reduction, error rates, balanced and unbalanced data, model selection, discrimination and classification, truncated and censored data, and experimental designs.
Each chapter offers readers problems and solutions and illustrative examples to introduce the concepts and methods discussed within.
The book offers:
Analysis of both classical theory and modern developments in the field of statistical inference and planning
Expansive discussions of linear and non-linear statistical inference
Statistical problems and solutions to test the reader’s progress through and retention of the material contained within
Author Biography
Subir Ghosh is a Professor of Statistics at the University of California, Riverside, USA. He is known for his research work in Statistical Design and Analysis of Experiments and Modeling. He is an elected fellow of the American Statistical Association, the American Association of the Advancement of Science, and an elected member of the International Statistical Institute. He received the awards at the University of California, Riverside:
2012-2016 Distinguished Teaching Professor and a member of the UCR Academy of Distinguished Teachers.
2003 Graduate Council Dissertation Advisor/Mentoring Award, and 1993 Academic Senate Distinguished Teaching Award.
He also served as the 2000-2003 executive editor of the Journal of Statistical Planning and Inference.
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