Nonparametric estimation of location and scale parameters

C.J. Potgieter and F. Lombard

Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 4327-4337

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.

Keywords: Location-scale families; Asymptotic likelihood; Nonparametric estimation (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:4327-4337

DOI: 10.1016/j.csda.2012.03.021

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