Local likelihood density estimation

Local likelihood density estimation based on smooth truncation

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 ...

On Local Likelihood Density Estimation When the Bandwidth Is Large

In this paper, we provide a large bandwidth analysis for a class of local likelihood methods. This work complements the small bandwidth analysis of Park, Kim and Jones (2002). Our treatment is more general than the large bandwidth analysis of Eguchi and Copas (1998). We provide a higher order asymptotic analysis for the risk of the local likelihood density estimator, from which a direct compari...

Penalized Likelihood Density Estimation :

Maximum likelihood kernel density estimation

Methods for improving the basic kernel density estimator include variable locations, variable bandwidths (often called variable kernels) and variable weights. Currently these methods are implemented separately and via pilot estimation of variation functions derived from asymptotic considerations. In this paper, we propose a simple maximum likelihood procedure which allows (in its greatest gener...

Partial Likelihood Methods for Probability Density Estimation

Partial likelihood (PL) establishes a sufficiently general framework to develop and study statistical properties of nonlinear techniques in signal processing. In [I], we present the theorem by which the fundamental information-theoretic relationship for learning on the P L cost, the equivalence of likelihood maximization and relative entropy minimization, is established. In this paper, we refor...