Nonparametric estimation of location and scale parameters

C.J. Potgieter, F. Lombard

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Two random variables X and Y belong to the same location-scale family if there are constants μ and σ such that Y and μ+σX have the same distribution. In this paper we consider non-parametric estimation of the parameters μ and σ under minimal assumptions regarding the form of the distribution functions of X and Y. We discuss an approach to the estimation problem that is based on asymptotic likelihood considerations. Our results enable us to provide a methodology that can be implemented easily and which yields estimators that are often near optimal when compared to fully parametric methods. We evaluate the performance of the estimators in a series of Monte Carlo simulations. © 2012 Elsevier B.V. All rights reserved.
Original languageEnglish (US)
Pages (from-to)4327-4337
Number of pages11
JournalComputational Statistics & Data Analysis
Volume56
Issue number12
DOIs
StatePublished - Dec 2012
Externally publishedYes

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