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Functional least squares estimators in an additive effects outliers model

Part of: Nonparametric inference Inference from stochastic processes

Published online by Cambridge University Press:  09 April 2009

Sunil K. Dhar
Sunil K. Dhar
Affiliation:
Department of MathematicsThe University of AlabamaP.O. Box 870350 Tuscaloosa, Alabama 35487-0350, U.S.A.
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Abstract

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Consider the additive effects outliers (A.O.) model where one observes , with The sequence of r.v.s is independent of and , are i.i.d. with d.f. , where the d.f.s Ln, n ≦ 0, are not necessarily known and εj's are i.i.d.. This paper discusses the asymptotic behavior of functional least squares estimators under the above model. Uniform consistency and uniform strong consistency of these estimators are proven. The weak convergence of these estimators to a Gaussian process and their asymptotic biases are also discussed under the above A.O. model.

MSC classification

Secondary: 62G05: Estimation 62M10: Time series, auto-correlation, regression, etc.
Type
Research Article
Information
Journal of the Australian Mathematical Society , Volume 48 , Issue 2 , April 1990 , pp. 299 - 319
Copyright
Copyright © Australian Mathematical Society 1990

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