Handle URI:
http://hdl.handle.net/10754/599000
Title:
Nonparametric estimation of location and scale parameters
Authors:
Potgieter, C.J.; Lombard, F.
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.
Citation:
Potgieter CJ, Lombard F (2012) Nonparametric estimation of location and scale parameters. Computational Statistics & Data Analysis 56: 4327–4337. Available: http://dx.doi.org/10.1016/j.csda.2012.03.021.
Publisher:
Elsevier BV
Journal:
Computational Statistics & Data Analysis
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Dec-2012
DOI:
10.1016/j.csda.2012.03.021
Type:
Article
ISSN:
0167-9473
Sponsors:
The first author's work was supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The second author's work was supported by the National Research Foundation of South Africa. The authors thank two referees for comments that led to an improved exposition of the work.
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Full metadata record

DC FieldValue Language
dc.contributor.authorPotgieter, C.J.en
dc.contributor.authorLombard, F.en
dc.date.accessioned2016-02-25T13:50:55Zen
dc.date.available2016-02-25T13:50:55Zen
dc.date.issued2012-12en
dc.identifier.citationPotgieter CJ, Lombard F (2012) Nonparametric estimation of location and scale parameters. Computational Statistics & Data Analysis 56: 4327–4337. Available: http://dx.doi.org/10.1016/j.csda.2012.03.021.en
dc.identifier.issn0167-9473en
dc.identifier.doi10.1016/j.csda.2012.03.021en
dc.identifier.urihttp://hdl.handle.net/10754/599000en
dc.description.abstractTwo 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.en
dc.description.sponsorshipThe first author's work was supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The second author's work was supported by the National Research Foundation of South Africa. The authors thank two referees for comments that led to an improved exposition of the work.en
dc.publisherElsevier BVen
dc.subjectAsymptotic likelihooden
dc.subjectLocation-scale familiesen
dc.subjectNonparametric estimationen
dc.titleNonparametric estimation of location and scale parametersen
dc.typeArticleen
dc.identifier.journalComputational Statistics & Data Analysisen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionUniversity of Johannesburg, Johannesburg, South Africaen
dc.contributor.institutionNorth-West University, Potchefstroom, South Africaen
kaust.grant.numberKUS-C1-016-04en
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