Titlebook: Bernoulli 1713 Bayes 1763 Laplace 1813; Anniversary Volume Jerzy Neyman,Lucien M. Cam Book 1965 Springer-Verlag Berlin · Heidelberg 1965 Ra

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Strong Limit Theorems for Stochastic Processes and Orthogonality Conditions for Probability MeasureLet . (.), 0 ≦ . ≦ ., be the Wiener process, that is, a real Gaussian stochastic process with . and let .. for . = 1, 2, ... be the sequence of increasing integers.

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Extension of the Kolmogorov-Smirnov Test to Regression Alternatives,tribution is derived in Section 4 by means of techniques developed in Section 1 (conditions for convergence in distribution in . [0, 1]) and Section 2 (extension of a Kolmogorov inequality to the case of sampling without replacement from a finite population). The auxiliary results just mentioned also offer some interest in their own right.

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Overview: 978-3-540-03260-1978-3-642-99884-3

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https://doi.org/10.1007/978-3-642-99884-3Random variable; distribution; probability theory; statistical inference; stochastic process