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dc.contributor.authorPotgieter, C.J.
dc.contributor.authorLombard, F.
dc.identifier.citationPotgieter, C.J. & Lombard, F. 2012. Nonparametric estimation of location and scale parameters. Computational statistics and data analysis, 56(12):4327-4337. []en_US
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.en_US
dc.subjectLocation-scale familiesen_US
dc.subjectasymptotic likelihooden_US
dc.subjectnonparametric estimationen_US
dc.titleNonparametric estimation of location and scale parametersen_US
dc.contributor.researchID12950149 - Lombard, Frederick

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