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Local likelihood density estimation based on smooth truncation

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  • Pedro Delicado

Abstract

Two existing density estimators based on local likelihood have properties that are comparable to those of local likelihood regression but they are much less used than their counterparts in regression. We consider truncation as a natural way of localising parametric density estimation. Based on this idea, a third local likelihood density estimator is introduced. Our main result establishes that the three estimators coincide when a free multiplicative constant is used as an extra local parameter. Copyright 2006, Oxford University Press.

Suggested Citation

  • Pedro Delicado, 2006. "Local likelihood density estimation based on smooth truncation," Biometrika, Biometrika Trust, vol. 93(2), pages 472-480, June.
    • Handle: RePEc:oup:biomet:v:93:y:2006:i:2:p:472-480
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    File URL: http://hdl.handle.net/10.1093/biomet/93.2.472
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    Cited by:

    1. Marco Marzio & Stefania Fensore & Agnese Panzera & Charles C. Taylor, 2018. "Circular local likelihood," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 921-945, December.

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