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June 1996 Fixed-design regression for linear time series
Lanh Tran, George Roussas, Sidney Yakowitz, B. Truong Van
Ann. Statist. 24(3): 975-991 (June 1996). DOI: 10.1214/aos/1032526952

Abstract

This investigation is concerned with recovering a regression function $g(x_i)$ on the basis of noisy observations taken at uniformly spaced design points $x_i$. It is presumed that the corresponding observations are corrupted by additive dependent noise, and that the noise is, in fact, induced by a general linear process in which the summand law can be discrete, as well as continuously distributed. Discreteness induces a complication because such noise is not known to be strong mixing, the postulate by which regression estimates are often shown to be asymptotically normal. In fact, as cited, there are processes of this character which have been proven not to be strong mixing. The main analytic result of this study is that, in general circumstances which include the non-strong mixing example, the smoothers we propose are asymptotically normal. Some motivation is offered, and a simple illustrative example calculation concludes this investigation. The innovative elements of this work, mainly, consist of compassing models with discrete noise, important in practical applications, and in dispensing with mixing assumptions. The ensuing mathematical difficulties are overcome by sharpening standard arguments.

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Lanh Tran. George Roussas. Sidney Yakowitz. B. Truong Van. "Fixed-design regression for linear time series." Ann. Statist. 24 (3) 975 - 991, June 1996. https://doi.org/10.1214/aos/1032526952

Information

Published: June 1996
First available in Project Euclid: 20 September 2002

zbMATH: 0862.62069
MathSciNet: MR1401833
Digital Object Identifier: 10.1214/aos/1032526952

Subjects:
Primary: 62G05
Secondary: 60J25, 62H12, 62J02, 62M05, 62M09

Rights: Copyright © 1996 Institute of Mathematical Statistics

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Vol.24 • No. 3 • June 1996
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