Constancy of distributions: nonparametric monitoring of probability distributions over time
AbstractIn this paper we study stochastic processes which enable monitoring thepossible changes of probability distributions over time. These processes mayin particular be used to test the null hypothesis of no change. Themonitoring processes are bivariate functions, of time and position at themeasurement scale, and are approximated with zero mean Gaussian processesunder the constancy hypothesis. One may then form Kolmogorov--Smirnov orother type of tests as functionals of the processes. To study nulldistributions of the resulting tests, we employ KMT-type inequalities toderive Cram\\'er-type deviation results for (bootstrapped versions of) suchtests statistics.
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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2001-50.
Date of creation: 31 Dec 2001
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stochastic processes; bivariate functions; nonparametric monitoring;
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