Nonparametric goodness-of-fit testing under Gaussian models
著者
書誌事項
Nonparametric goodness-of-fit testing under Gaussian models
（Lecture notes in statistics, 169）
Springer, c2003
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注記
Includes bibliographical references (p. [444]-449) and indexes
内容説明・目次
内容説明
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.
目次
Introduction * An Overview * Minimax Distinguishability * Sharp Asymptotics. I * Sharp Asymptotics. II * Gaussian Asymptotics for Power and Besov Norms * Adaptation for Power and Besov Norms * High-Dimensional Signal Detection * Appendix
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