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:
http://cm.bell-labs.com/stat/project/locfit/index.html
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Mathematical Reviews (MathSciNet):
MR898359
MURRAY HILL, NEW JERSEY 07974-2070 E-MAIL: clive@bell-labs.com
