DOIAbstract
Kernel smoothing, Local linear regression, Semiparametric density estimation, Transformations,
Kernel smoothing, Local linear regression, Semiparametric density estimation, Transformations,
Four nonparametric estimates of the mode of a density function are investigated. Two mode estimates ...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
Epanechnikov kernel, Local polynomial regression, Non-invertible moving average processes,
By drawing an analogy with likelihood for censored data, a local likelihood function is proposed whi...
This paper develops a nonparametric density estimator with parametric overtones. Suppose f(x, θ) is ...
Two existing density estimators based on local likelihood have properties that are comparable to t...
Recent papers of Copas (1995), Hjort and Jones (1996) and Loader (1996) have developed closely relat...
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. ...
Summary. The density ratio model specifies that the likelihood ratio of m−1 probability density func...
This paper considers a class of local likelihood methods produced by Eguchi and Copas. Unified asym...
The paper gives an introduction to theory and application of multivariate and semipara metric kernel...
Consistency, Jump-preserving estimation, Local linear fit, Nonparametric regression, Smoothing, Weig...
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN020063 / BLDSC - British Library D...
We propose a new estimator for boundary correction for kernel density estimation. Our method is base...
We revisit a semiparametric procedure for density estimation based on a convex combination of a nonp...
Four nonparametric estimates of the mode of a density function are investigated. Two mode estimates ...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
Epanechnikov kernel, Local polynomial regression, Non-invertible moving average processes,
By drawing an analogy with likelihood for censored data, a local likelihood function is proposed whi...
This paper develops a nonparametric density estimator with parametric overtones. Suppose f(x, θ) is ...
Two existing density estimators based on local likelihood have properties that are comparable to t...
Recent papers of Copas (1995), Hjort and Jones (1996) and Loader (1996) have developed closely relat...
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. ...
Summary. The density ratio model specifies that the likelihood ratio of m−1 probability density func...
This paper considers a class of local likelihood methods produced by Eguchi and Copas. Unified asym...
The paper gives an introduction to theory and application of multivariate and semipara metric kernel...
Consistency, Jump-preserving estimation, Local linear fit, Nonparametric regression, Smoothing, Weig...
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN020063 / BLDSC - British Library D...
We propose a new estimator for boundary correction for kernel density estimation. Our method is base...
We revisit a semiparametric procedure for density estimation based on a convex combination of a nonp...
Four nonparametric estimates of the mode of a density function are investigated. Two mode estimates ...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
Epanechnikov kernel, Local polynomial regression, Non-invertible moving average processes,
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