Martingale transforms goodness-of-fit tests in regression models

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dc.creatorKhmaladze, Estate V.
dc.creatorKoul, Hira L.
dc.date2004-06-25
dc.date.accessioned2026-07-07T08:06:23Z
dc.date.available2026-07-07T08:06:23Z
dc.descriptionThis paper discusses two goodness-of-fit testing problems. The first problem pertains to fitting an error distribution to an assumed nonlinear parametric regression model, while the second pertains to fitting a parametric regression model when the error distribution is unknown. For the first problem the paper contains tests based on a certain martingale type transform of residual empirical processes. The advantage of this transform is that the corresponding tests are asymptotically distribution free. For the second problem the proposed asymptotically distribution free tests are based on innovation martingale transforms. A Monte Carlo study shows that the simulated level of the proposed tests is close to the asymptotic level for moderate sample sizes.
dc.identifierhttps://arxiv.org/abs/math/0406518
dc.identifierhttp://arxiv.org/abs/math/0406518
dc.identifierAnnals of Statistics 2004, Vol. 32, No. 3, 995-1034
dc.identifierdoi:10.1214/009053604000000274
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130587
dc.subjectStatistics Theory
dc.subject62G10 (Primary) 62J02. (Secondary)
dc.titleMartingale transforms goodness-of-fit tests in regression models
dc.typetext

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