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A class of asymptotically efficient estimators based on sample spacings

Ekström, Magnus and Mirakhmedov, S. M. and Rao Jammalamadaka, S. (2020). A class of asymptotically efficient estimators based on sample spacings. TEST. 29 , 617-636
[Journal article]

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Abstract

In this paper, we consider general classes of estimators based on higher-order sample spacings, called the Generalized Spacings Estimators. Such classes of estimators are obtained by minimizing the Csiszar divergence between the empirical and true distributions for various convex functions, include the "maximum spacing estimators" as well as the maximum likelihood estimators (MLEs) as special cases, and are especially useful when the latter do not exist. These 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. These estimators are shown to be consistent as well as asymptotically normal under a fairly general set of regularity conditions. When the step size and the number of spacings grow with the sample size, an asymptotically efficient class of estimators, called the "Minimum Power Divergence Estimators," are shown to exist. 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.

Authors/Creators:Ekström, Magnus and Mirakhmedov, S. M. and Rao Jammalamadaka, S.
Title:A class of asymptotically efficient estimators based on sample spacings
Year of publishing :2020
Volume:29
Page range:617-636
Number of Pages:20
Publisher:Springer
ISSN:1133-0686
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 101 Mathematics > 10106 Probability Theory and Statistics
Keywords:Sample spacings, Estimation, Asymptotic efficiency, Robustness
URN:NBN:urn:nbn:se:slu:epsilon-p-103622
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-103622
Additional ID:
Type of IDID
DOI10.1007/s11749-019-00637-7
Web of Science (WoS)000563156900001
ID Code:17556
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:21 Sep 2020 07:00
Metadata Last Modified:21 Sep 2020 07:07

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