Some convergence results on the kernel density estimator are proven for a class of linear processes with cyclic effects. In particular, we extend the results of Ho and Hsing (1996), Mielniczuk (1997) and Hall and Hart (1990) to the stationary processes for which the singularities of the spectral density are not limited to the origin. We show that the convergence rates and the limiting distribution may be different in this context
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
AbstractWeakly and strongly consistent nonparametric estimates, along with rates of convergence, are...
AbstractIn this paper moving-average processes with no parametric assumption on the error distributi...
International audienceSome convergence results on the kernel density estimator are proven for a clas...
We specify conditions under which kernel density estimate for linear process is weakly and strongly ...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Let X1,...,Xn be n consecutive observations of a linear process , where [mu] is a constant and {Zt} ...
This paper studies the asymptotic properties of the kernel probability density estimate of stationar...
February 2006; August 2006 (Revised)We consider nonparametric estimation of marginal density functio...
AbstractLet X1,…,Xn be n consecutive observations of a linear process X1=μ+∑r=0∞ArZt−r, where μ is a...
AbstractThis paper studies the asymptotic properties of the kernel probability density estimate of s...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
AbstractWeakly and strongly consistent nonparametric estimates, along with rates of convergence, are...
AbstractIn this paper moving-average processes with no parametric assumption on the error distributi...
International audienceSome convergence results on the kernel density estimator are proven for a clas...
We specify conditions under which kernel density estimate for linear process is weakly and strongly ...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Rate of convergence to normality for the density estimators of Kernel type is obtained when the obse...
Let X1,...,Xn be n consecutive observations of a linear process , where [mu] is a constant and {Zt} ...
This paper studies the asymptotic properties of the kernel probability density estimate of stationar...
February 2006; August 2006 (Revised)We consider nonparametric estimation of marginal density functio...
AbstractLet X1,…,Xn be n consecutive observations of a linear process X1=μ+∑r=0∞ArZt−r, where μ is a...
AbstractThis paper studies the asymptotic properties of the kernel probability density estimate of s...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
We consider the estimation of the location of the pole and memory parameter ω0 and d of a covariance...
AbstractWeakly and strongly consistent nonparametric estimates, along with rates of convergence, are...
AbstractIn this paper moving-average processes with no parametric assumption on the error distributi...