Titlebook: Bootstrapping and Related Techniques; Proceedings of an In Karl-Heinz J?ckel,Günter Rothe,Wolfgang Sendler Conference proceedings 1992 Spri

Forehead-Lift

https://doi.org/10.1007/978-3-642-48850-4Biometrie; Bootstrap; Bootstrapping; Estimator; Generalized linear model; Jackknife; Monte Carlo Test; Prob

Annotate

978-3-540-55003-7Springer-Verlag Berlin Heidelberg 1992

Intellectual

0075-8442 0. Thepurpose of the book is to in-form about recent research in the area of bootstrap, jack-knife and Monte Carlo Tests. Addressing the novice and theexpert it covers as well theoretical as practical aspects ofthesestatistical techniques. Potential users in differentdisciplines as biometry, epidemi

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Conscientious

City System Based on the Rank-Size Rule statistic to be tested for ..: . = ... To test this hypothesis Beran proposes two alternative approaches. The first one which is called the . approximates the exact critical region of the test by the percentiles of its bootstrap distribution, where the unknown distribution of the sample is replaced by its empirical distribution.

constitutional

City System Based on the Rank-Size Rulesed on permutations of the data, may be conveniently used to combine the k test statistics, when they are characterized by an unknown dependence structure. These techniques have recently been interpreted (Hinkley (1989), Pesarin (1989) and Romano (1989)) as conditional bootstrap methods.

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recede

Principles for Generating Non-Uniform Random Numbers-random numbers in the interval [0,1). The constants ., the modulus, ., the multiplicator, ., the increment, and .., the starting number are suitably chosen non-negative integers. Three choices of ., . and . are common on most computers:

分期付款

Nonparametric Bootstrap Tests: Some Applications statistic to be tested for ..: . = ... To test this hypothesis Beran proposes two alternative approaches. The first one which is called the . approximates the exact critical region of the test by the percentiles of its bootstrap distribution, where the unknown distribution of the sample is replaced by its empirical distribution.

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