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August 1996 Local likelihood density estimation
Clive R. Loader
Ann. Statist. 24(4): 1602-1618 (August 1996). DOI: 10.1214/aos/1032298287


Local likelihood was introduced by Tibshirani and Hastie as a method of smoothing by local polynomials in non-Gaussian regression models. In this paper an extension of these methods to density estimation is discussed, and comparison with other methods of density estimation presented. The local likelihood method has particularly strong advantages over kernel methods when estimating tails of densities and in multivariate settings. Suppose constraints are incorporated in a simple manner. Asymptotic properties of the estimate are discussed. A method for computing the estimate is outlined.

C code to implement the estimation procedure described in this paper, together with S interfaces for graphical display of results, are available at:


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Clive R. Loader. "Local likelihood density estimation." Ann. Statist. 24 (4) 1602 - 1618, August 1996.


Published: August 1996
First available in Project Euclid: 17 September 2002

zbMATH: 0867.62034
MathSciNet: MR1416652
Digital Object Identifier: 10.1214/aos/1032298287

Primary: 62G07
Secondary: 62G20 , 62H12

Keywords: Density estimation , local likelihood , local polynomials

Rights: Copyright © 1996 Institute of Mathematical Statistics


Vol.24 • No. 4 • August 1996
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