Rank-based estimation for all-pass time series models

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dc.creatorAndrews, Beth
dc.creatorDavis, Richard A.
dc.creatorBreidt, F. Jay
dc.date2007-08-14
dc.date.accessioned2026-07-07T08:24:37Z
dc.date.available2026-07-07T08:24:37Z
dc.descriptionAn autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models are useful for identifying and modeling noncausal and noninvertible autoregressive-moving average processes. We establish asymptotic normality and consistency for rank-based estimators of all-pass model parameters. The estimators are obtained by minimizing the rank-based residual dispersion function given by Jaeckel [Ann. Math. Statist. 43 (1972) 1449--1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The behavior of the estimators for finite samples is studied via simulation and rank estimation is used in the deconvolution of a simulated water gun seismogram.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053606000001316 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/0708.1929
dc.identifierhttp://arxiv.org/abs/0708.1929
dc.identifierAnnals of Statistics 2007, Vol. 35, No. 2, 844-869
dc.identifierdoi:10.1214/009053606000001316
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/136402
dc.subjectStatistics Theory
dc.subject62M10 (Primary); 62E20, 62F10 (Secondary)
dc.titleRank-based estimation for all-pass time series models
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