On asymptotically distribution free tests with parametric hypothesis for ergodic diffusion processes

M. Kleptsyna and Yu. Kutoyants ()

Statistical Inference for Stochastic Processes, 2014, vol. 17, issue 3, 295-319

Abstract: We consider the problem of the construction of the asymptotically distribution free test by the observations of ergodic diffusion process. It is supposed that under the basic hypothesis the trend coefficient depends on a finite-dimensional parameter and we study the Cramér-von Mises type statistics. The underlying statistics depends on the deviation of the local time estimator from the invariant density with parameter replaced by the maximum likelihood estimator. We propose a linear transformation which yields the convergence of the test statistics to an integral of the Wiener process. Therefore the test based on this statistics is asymptotically distribution free. Copyright Springer Science+Business Media Dordrecht 2014

Keywords: Cramér-von Mises tests; Ergodic diffusion process; Goodness of fit test; Asymptotically distribution free; 62M02; 62G10; 62G20 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:sistpr:v:17:y:2014:i:3:p:295-319

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DOI: 10.1007/s11203-014-9096-3

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