Series: Wiley Series in Probability and Statistics. Num Pages: 512 pages. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 244 x 170. . . 2025. 1st Edition. hardcover. . . . .

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Bibliographic Details

Title: Statistical Planning and Inference: Concepts and Applications (Wiley Series in Probability and Statistics)
Publisher: Wiley
Publication Date: 2025
Language: English
ISBN 10: 1119962781
ISBN 13: 9781119962786
Binding: Hardcover
Edition: 1st Edition
Condition: New

About this title

Synopsis

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

Aimed at practitioners and researchers in the field of statistics, Statistical Planning and Inference: Concepts and Applications is also a must-read resource for graduate students, professors, and researchers in the life sciences, agriculture, psychology, education and measurement, sociology, computer and engineering sciences, and all other fields that rely on statistical concepts.

About the Author

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.

"About this title" may belong to another edition of this title.

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