Nonparametric Goodness-of-fit Testing Under Gaussian Models
Ingster, Yu I.; Suslina, I.A.
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Description for Nonparametric Goodness-of-fit Testing Under Gaussian Models
Paperback. Suitable for researchers working in the area of nonparametric statistics. Series: Lecture Notes in Statistics. Num Pages: 471 pages, 1 black & white illustrations, biography. BIC Classification: PBK; PBT. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 235 x 155 x 24. Weight in Grams: 662.
This book presents the modern theory of nonparametric goodness-of-fit testing. It fills the gap in modern nonparametric statistical theory by discussing hypothesis testing and addresses mathematical statisticians who are interesting in the theory of non-parametric statistical inference. It will be of interest to specialists who are dealing with applied non-parametric statistical problems relevant in signal detection and transmission and in technical and medical diagnostics among others.
This book presents the modern theory of nonparametric goodness-of-fit testing. It fills the gap in modern nonparametric statistical theory by discussing hypothesis testing and addresses mathematical statisticians who are interesting in the theory of non-parametric statistical inference. It will be of interest to specialists who are dealing with applied non-parametric statistical problems relevant in signal detection and transmission and in technical and medical diagnostics among others.
Product Details
Format
Paperback
Publication date
2002
Publisher
Springer-Verlag New York Inc. United States
Number of pages
471
Condition
New
Series
Lecture Notes in Statistics
Number of Pages
457
Place of Publication
New York, NY, United States
ISBN
9780387955315
SKU
V9780387955315
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Nonparametric Goodness-of-fit Testing Under Gaussian Models
From the reviews: "The book is self-contained, and the bibliography is very rich and in fact provides a comprehensive listing of references about minimax testing (something that heretofore had been missing from the field.) To get the best out of this book, the reader should be familiar with basic functional analysis, wavelet theory, and optimization for extreme problems…It ... Read more