Distribution free goodness-of-fit tests for linear processes

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dc.creatorDelgado, Miguel A.
dc.creatorHidalgo, Javier
dc.creatorVelasco, Carlos
dc.date2006-03-02
dc.date.accessioned2026-07-07T08:07:36Z
dc.date.available2026-07-07T08:07:36Z
dc.descriptionThis 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.
dc.descriptionPublished 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)
dc.identifierhttps://arxiv.org/abs/math/0603043
dc.identifierhttp://arxiv.org/abs/math/0603043
dc.identifierAnnals of Statistics 2005, Vol. 33, No. 6, 2568-2609
dc.identifierdoi:10.1214/009053605000000606
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130988
dc.subjectStatistics Theory
dc.subject62G10, 62M10 (Primary) 62F17, 62M15 (Secondary)
dc.titleDistribution free goodness-of-fit tests for linear processes
dc.typetext

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