Robust estimation in nonlinear regression via minimum distance method

Mukherjee, Kanchan (1996) Robust estimation in nonlinear regression via minimum distance method. Mathematical Methods of Statistics, 5. pp. 99-112. ISSN 1934-8045

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Abstract

We study the asymptotic properties of a general class of minimum distance estimators based on L2 norms of weighted empirical processes in nonlinear regression models. In particular, the asymptotic uniform quadratic structure of the minimum distance statistics and the asymptotic representation of the estimator are established under weak conditions on the nonlinear function and under some non-i.i.d. structures of the error variables. The results imply the asymptotic normality of the estimator and its qualitative robustness. Applications are given to the problem of goodness-of-fit tests for the error distribution.

Item Type: Journal Article
Journal or Publication Title: Mathematical Methods of Statistics
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 65666
Deposited By: ep_importer_pure
Deposited On: 15 Jul 2013 09:10
Refereed?: Yes
Published?: Published
Last Modified: 30 Sep 2019 17:34
URI: https://eprints.lancs.ac.uk/id/eprint/65666

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