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A class of asymptotically efficient estimators based on sample spacings
Swedish University of Agricultural Sciences, Umeå, Sweden.
2019 (engelsk)Inngår i: EMS 2019 - Program and Book of Abstracts / [ed] Angelo M. Mineo, Luigi Augugliaro, 2019, s. 95-95Konferansepaper, Oral presentation with published abstract (Fagfellevurdert)
Abstract [en]

We consider a general class of estimators based on higher-order sample spacings, called Minimum Power Divergence Estimators (MPDEs). Such estimators are obtained by minimizing approximations of so-called power divergences between distributions in the model and the true underlying distribution, and include the maximum product of spacings estimator as a special case. The maximum likelihood estimator (MLE) may be derived in a similar way using the Kullback-Leibler divergence. Spacings-based estimators are especially useful when MLEs do not exist. Our results generalize several earlier studies on spacings-based estimation, by utilizing non-overlapping spacings that are of an order which increases with the sample size. The MPDEs are shown to be consistent as well as asymptotically efficient under a fairly general set of regularity conditions. Simulation studies give further support to the performance of these asymptotically efficient estimators in finite samples, and compare well relative to the MLEs. Unlike the MLEs, some of these estimators are also shown to be quite robust under heavy contamination.

sted, utgiver, år, opplag, sider
2019. s. 95-95
HSV kategori
Forskningsprogram
statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-163298OAI: oai:DiVA.org:umu-163298DiVA, id: diva2:1350881
Konferanse
32nd edition of the European Meeting of Statisticians, Palermo (Italy), 22–26 July 2019
Tilgjengelig fra: 2019-09-12 Laget: 2019-09-12 Sist oppdatert: 2019-09-16bibliografisk kontrollert

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https://www.ems2019.palermo.it/EMS19-Book_of_Abstracts.pdf

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