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Titlebook: Concepts of Nonparametric Theory; John W. Pratt,Jean D. Gibbons Book 1981 Spring-Verlag New York Inc. 1981 Mathematica.Parameterfreier Tes

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發(fā)表于 2025-3-21 17:38:44 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Concepts of Nonparametric Theory
編輯John W. Pratt,Jean D. Gibbons
視頻videohttp://file.papertrans.cn/235/234944/234944.mp4
叢書名稱Springer Series in Statistics
圖書封面Titlebook: Concepts of Nonparametric Theory;  John W. Pratt,Jean D. Gibbons Book 1981 Spring-Verlag New York Inc. 1981 Mathematica.Parameterfreier Tes
描述This book explores both non parametric and general statistical ideas by developing non parametric procedures in simple situations. The major goal is to give the reader a thorough intuitive understanding of the concepts underlying nonparametric procedures and a full appreciation of the properties and operating characteristics of those procedures covered. This book differs from most statistics books by including considerable philosophical and methodological discussion. Special attention is given to discussion of the strengths and weaknesses of various statistical methods and approaches. Difficulties that often arise in applying statistical theory to real data also receive substantial attention. The approach throughout is more conceptual than mathematical. The "Theorem-Proof" format is avoided; generally, properties are "shown," rather than "proved." In most cases the ideas behind the proof of an im- portant result are discussed intuitively in the text and formal details are left as an exercise for the reader. We feel that the reader will learn more from working such things out than from checking step-by-step a complete presen- tation of all details.
出版日期Book 1981
關鍵詞Mathematica; Parameterfreier Test; Permutation; algorithms; boundary element method; constant; constructio
版次1
doihttps://doi.org/10.1007/978-1-4612-5931-2
isbn_softcover978-1-4612-5933-6
isbn_ebook978-1-4612-5931-2Series ISSN 0172-7397 Series E-ISSN 2197-568X
issn_series 0172-7397
copyrightSpring-Verlag New York Inc. 1981
The information of publication is updating

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One-Sample and Paired-Sample Inferences Based on the Binomial Distribution,ating process. The inferences may concern any aspect of a suitably defined population (or process) from which observations are obtained, for example, the form or shape of the probability distribution of some variable in the population, or any definable properties, characteristics or parameters of th
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One-Sample and Paired-Sample Inferences Based on Signed Ranks,the statement “the observations are a random sample from a population with median 0 (or ξ.).” It certainly seems as though better use could be made of the observations by taking their magnitudes into account. However, since the sign test is optimum at level a for this inclusive set of null distribut
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One-Sample and Paired-Sample Inferences Based on the Method of Randomization,ations are equally likely under the null hypothesis of distributions symmetric about zero, or about some arbitrary point which is subtracted from each observation before ranking. The same fact is true also for the observations themselves, not merely for their ranks, and the idea underlying these tes
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Asymptotic Relative Efficiency,ts is needed, especially as regards their performance or operating characteristics. For instance, a comparison of the power functions of various tests of the same (or essentially the same) hypotheses would be of interest. It is frequently more convenient, and also more suggestive, to use a measure o
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