Add to ORKGThis note considers a new class of nonparametric estimators for nonlinear time-series models based on kernel smoothers. Various new results are given for two popular nonlinear time-series models and compared with the results of Thavaneswaran and Peiris (Statist. Probab. Lett. 28 (1996) 227).Non parametric Kernel density Generalized estimators Nonlinear Time series Estimating functions Smoothing
The asymptotic mean integrated squared error (AMISE) and the kernel efficiency (KE) of kernel distri...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
We provide a common approach for studying several nonparametric estimators used for smoothing functi...
Godambe's (1985) theorem on optimal estimating equations for stochastic processes is applied to nonp...
Kernel density estimators have been studied in great detail. In this note a new family of kernels, d...
We introduce a new class of nonparametric density estimators. It includes the classical kernel densi...
We provide a common approach for studying several nonparametric estimators used for smoothing functi...
Nonparametric kernel estimation of density is widely used, how-ever, many of the pointwise and globa...
This paper develops recursive kernel estimators for the probability density and the regression funct...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. ...
Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space mod...
We address the problem of prediction of nonlinear time series by kernel estimation of autoregression...
AbstractA general framework for analyzing estimates in nonlinear time series is developed. General c...
We provide a common approach for studying several nonparametric estimators used for smoothing functi...
The asymptotic mean integrated squared error (AMISE) and the kernel efficiency (KE) of kernel distri...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
We provide a common approach for studying several nonparametric estimators used for smoothing functi...
Godambe's (1985) theorem on optimal estimating equations for stochastic processes is applied to nonp...
Kernel density estimators have been studied in great detail. In this note a new family of kernels, d...
We introduce a new class of nonparametric density estimators. It includes the classical kernel densi...
We provide a common approach for studying several nonparametric estimators used for smoothing functi...
Nonparametric kernel estimation of density is widely used, how-ever, many of the pointwise and globa...
This paper develops recursive kernel estimators for the probability density and the regression funct...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. ...
Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space mod...
We address the problem of prediction of nonlinear time series by kernel estimation of autoregression...
AbstractA general framework for analyzing estimates in nonlinear time series is developed. General c...
We provide a common approach for studying several nonparametric estimators used for smoothing functi...
The asymptotic mean integrated squared error (AMISE) and the kernel efficiency (KE) of kernel distri...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
We provide a common approach for studying several nonparametric estimators used for smoothing functi...