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This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.
This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at
Probability theory; Statistical inference; Some tests based on the binomial distribution; Contingency tables; Some methods based on ranks; Statistics of the koo
A practical and understandable approach to nonparametric statistics for researchers across diverse areas of study As the importance of nonparametric methods in
“...a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in
A thorough and definitive book that fully addresses traditional and modern-day topics of nonparametric statistics This book presents a practical approach to non
