Add to ORKGWe consider the nonparametric estimation of the distribution of innovations εt in a stationary autoregression. We obtain estimators of the kernel of the probability density of εt and its derivatives from the estimated residuals of the Yule-Walker estimator of the autoregressive coefficients
The problem of predicting a future value of a time series is considered in this paper. If the series...
February 2006; August 2006 (Revised)We consider nonparametric estimation of marginal density functio...
AbstractSuppose we observe a time series that alternates between different nonlinear autoregressive ...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
In a time series regression model the residual autoregression function is an unknown, possibly non-l...
Godambe's (1985) theorem on optimal estimating equations for stochastic processes is applied to nonp...
We illustrate several recent results on efficient estimation for semiparametric time series models w...
In this paper, we consider the problem of estimating the marginal density in some nonlinear autoregr...
Suppose we observe a time series that alternates between different nonlinear autoregressive processe...
In many applications of time series, the assumption of stationarity has been widely used to analyse ...
14 pagesThe paper deals with the nonparametric estimation problem at a given fixed point for an auto...
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...
We obtain semiparametric efficiency bounds for estimation of a location parameter in a time series m...
The estimation of coefficients in a simple autoregressive model is considered in a supposedly diffic...
We consider a nonparametric method to estimate copulas, i.e. functions linking joint distributions t...
The problem of predicting a future value of a time series is considered in this paper. If the series...
February 2006; August 2006 (Revised)We consider nonparametric estimation of marginal density functio...
AbstractSuppose we observe a time series that alternates between different nonlinear autoregressive ...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
In a time series regression model the residual autoregression function is an unknown, possibly non-l...
Godambe's (1985) theorem on optimal estimating equations for stochastic processes is applied to nonp...
We illustrate several recent results on efficient estimation for semiparametric time series models w...
In this paper, we consider the problem of estimating the marginal density in some nonlinear autoregr...
Suppose we observe a time series that alternates between different nonlinear autoregressive processe...
In many applications of time series, the assumption of stationarity has been widely used to analyse ...
14 pagesThe paper deals with the nonparametric estimation problem at a given fixed point for an auto...
We propose new estimation methods for time series models, possibly noncausal and/or noninvertible, u...
We obtain semiparametric efficiency bounds for estimation of a location parameter in a time series m...
The estimation of coefficients in a simple autoregressive model is considered in a supposedly diffic...
We consider a nonparametric method to estimate copulas, i.e. functions linking joint distributions t...
The problem of predicting a future value of a time series is considered in this paper. If the series...
February 2006; August 2006 (Revised)We consider nonparametric estimation of marginal density functio...
AbstractSuppose we observe a time series that alternates between different nonlinear autoregressive ...