Recent papers of Copas (1995), Hjort and Jones (1996) and Loader (1996) have developed closely related methods for “local likelihood” density estimation. In various places, however, a more “simple-minded” and explicit analogue of local polynomial fitting in regression has been proposed for density estimation. By introducing the usual kind of binning procedure into Hjor and Jones' method, it is shown how the more and less sophisticated versions can be reconciled. Also, we attempt to understand better the role of the attractive subclass of local likelihood methodology proposed by Loader. Finally, we look at a further subset of methods and make some theoretical comparisons between members of this class
Although Hartigan (1975) had already put forward the idea of connecting identification of subpopulat...
The paper presents a unified approach to local likelihood estimation for a broad class of nonparamet...
在非參數判別分析中,我們利用區間Logistic迴歸模型 (Local logistic regression)估計貝氏準則的事後機率。在進行區間Logistic迴歸時,我們需要決定平滑參數值,我們取...
Methods for probability density estimation are traditionally classified as either parametric or non-...
This paper considers a class of local likelihood methods produced by Eguchi and Copas. Unified asym...
Two existing density estimators based on local likelihood have properties that are comparable to t...
By drawing an analogy with likelihood for censored data, a local likelihood function is proposed whi...
Kernel smoothing, Local linear regression, Semiparametric density estimation, Transformations,
This paper develops a nonparametric density estimator with parametric overtones. Suppose f(x, θ) is ...
The local likelihood estimator and a semiparametric bootstrap method are studied under weaker condit...
In this article, a naive empirical likelihood ratio is constructed for a non-parametric regression m...
Recent work in the field of probability density estimation has included the introduction of some new...
Paper 1 ”Bias and bandwidth for local likelihood density estimation”: A local likelihood density est...
De Bruin et al. (Comput. Statist. Data Anal. 30 (1999) 19) provide a unique method to estimate the p...
This article introduces an intuitive and easy-to-implement nonparametric density estimator based on ...
Although Hartigan (1975) had already put forward the idea of connecting identification of subpopulat...
The paper presents a unified approach to local likelihood estimation for a broad class of nonparamet...
在非參數判別分析中,我們利用區間Logistic迴歸模型 (Local logistic regression)估計貝氏準則的事後機率。在進行區間Logistic迴歸時,我們需要決定平滑參數值,我們取...
Methods for probability density estimation are traditionally classified as either parametric or non-...
This paper considers a class of local likelihood methods produced by Eguchi and Copas. Unified asym...
Two existing density estimators based on local likelihood have properties that are comparable to t...
By drawing an analogy with likelihood for censored data, a local likelihood function is proposed whi...
Kernel smoothing, Local linear regression, Semiparametric density estimation, Transformations,
This paper develops a nonparametric density estimator with parametric overtones. Suppose f(x, θ) is ...
The local likelihood estimator and a semiparametric bootstrap method are studied under weaker condit...
In this article, a naive empirical likelihood ratio is constructed for a non-parametric regression m...
Recent work in the field of probability density estimation has included the introduction of some new...
Paper 1 ”Bias and bandwidth for local likelihood density estimation”: A local likelihood density est...
De Bruin et al. (Comput. Statist. Data Anal. 30 (1999) 19) provide a unique method to estimate the p...
This article introduces an intuitive and easy-to-implement nonparametric density estimator based on ...
Although Hartigan (1975) had already put forward the idea of connecting identification of subpopulat...
The paper presents a unified approach to local likelihood estimation for a broad class of nonparamet...
在非參數判別分析中,我們利用區間Logistic迴歸模型 (Local logistic regression)估計貝氏準則的事後機率。在進行區間Logistic迴歸時,我們需要決定平滑參數值,我們取...