Distribution free goodness-of-fit tests for linear processes

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This article proposes a class of goodness-of-fit tests for the autocorrelation function of a time series process, including those exhibiting long-range dependence. Test statistics for composite hypotheses are functionals of a (approximated) martingale transformation of the Bartlett $T_p$-process with estimated parameters, which converges in distribution to the standard Brownian motion under the null hypothesis. We discuss tests of different natures such as omnibus, directional and Portmanteau-type tests. A Monte Carlo study illustrates the performance of the different tests in practice.
Published at http://dx.doi.org/10.1214/009053605000000606 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Statistics Theory, 62G10, 62M10 (Primary) 62F17, 62M15 (Secondary)

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http://salesiana.dossiersoluciones.com/handle/123456789/130988

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/math/0603043
 http://arxiv.org/abs/math/0603043
 Annals of Statistics 2005, Vol. 33, No. 6, 2568-2609
 doi:10.1214/009053605000000606

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arXiv