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.Peer Reviewe
We revisit a semiparametric procedure for density estimation based on a convex combination of a nonp...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
De Bruin et al. (Comput. Statist. Data Anal. 30 (1999) 19) provide a unique method to estimate the p...
Two existing density estimators based on local likelihood have properties that are comparable to t...
This paper develops a nonparametric density estimator with parametric overtones. Suppose f(x, θ) is ...
Methods for probability density estimation are traditionally classified as either parametric or non-...
By drawing an analogy with likelihood for censored data, a local likelihood function is proposed whi...
Recent papers of Copas (1995), Hjort and Jones (1996) and Loader (1996) have developed closely relat...
In this paper we propose a very flexible estimator in the context of truncated regression that does ...
A novel semiparametric estimator for the probability density function of detected distances in line ...
Paper 1 ”Bias and bandwidth for local likelihood density estimation”: A local likelihood density est...
In this paper we propose a very flexible estimator in the context of truncated regression that does n...
The paper presents a unified approach to local likelihood estimation for a broad class of nonparamet...
A 'skewing' method is shown to effectively reduce the order of bias of locally parametric estimators...
This paper considers a class of local likelihood methods produced by Eguchi and Copas. Unified asym...
We revisit a semiparametric procedure for density estimation based on a convex combination of a nonp...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
De Bruin et al. (Comput. Statist. Data Anal. 30 (1999) 19) provide a unique method to estimate the p...
Two existing density estimators based on local likelihood have properties that are comparable to t...
This paper develops a nonparametric density estimator with parametric overtones. Suppose f(x, θ) is ...
Methods for probability density estimation are traditionally classified as either parametric or non-...
By drawing an analogy with likelihood for censored data, a local likelihood function is proposed whi...
Recent papers of Copas (1995), Hjort and Jones (1996) and Loader (1996) have developed closely relat...
In this paper we propose a very flexible estimator in the context of truncated regression that does ...
A novel semiparametric estimator for the probability density function of detected distances in line ...
Paper 1 ”Bias and bandwidth for local likelihood density estimation”: A local likelihood density est...
In this paper we propose a very flexible estimator in the context of truncated regression that does n...
The paper presents a unified approach to local likelihood estimation for a broad class of nonparamet...
A 'skewing' method is shown to effectively reduce the order of bias of locally parametric estimators...
This paper considers a class of local likelihood methods produced by Eguchi and Copas. Unified asym...
We revisit a semiparametric procedure for density estimation based on a convex combination of a nonp...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
De Bruin et al. (Comput. Statist. Data Anal. 30 (1999) 19) provide a unique method to estimate the p...