In this paper, the central limit theorems for the density estimator and for the integrated square error are proved for the case when the underlying sequence of random variables is nonstationary. Applications to Markov processes and ARMA processes are provided
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
The asymptotic behavior of nonparametric estimators of the probability density function of an i.i.d....
The Gaussian kernel density estimator is known to have substantial problems for bounded random varia...
AbstractThe nonparametric estimation results for time series described in the literature to date ste...
International audienceLet X m CXt,t>0] be a stationary stochastic process and suppose XQ has a proba...
For a stationary sequence $ { X_i } $ the Markov assumption $ G_2 $, which is weaker than the Doebli...
AbstractThis paper studies the asymptotic properties of the kernel probability density estimate of s...
This paper studies the asymptotic properties of the kernel probability density estimate of stationar...
Martingale theory is used to obtain a central limit theorem for degenerate U-statistics with variabl...
The nonparametric estimation results for time series described in the literature to date stem fairly...
In this paper, we study the problem of the nonparametric estimation of the marginal density f of a c...
AbstractIn this paper, we study the problem of the nonparametric estimation of the marginal density ...
AbstractIn order to construct confidence sets for a marginal density f of a strictly stationary cont...
AbstractA general nonparametric density estimation problem is considered in which the data is genera...
It has been shown recently that, under an appropriate integra-bility condition, densities of functio...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
The asymptotic behavior of nonparametric estimators of the probability density function of an i.i.d....
The Gaussian kernel density estimator is known to have substantial problems for bounded random varia...
AbstractThe nonparametric estimation results for time series described in the literature to date ste...
International audienceLet X m CXt,t>0] be a stationary stochastic process and suppose XQ has a proba...
For a stationary sequence $ { X_i } $ the Markov assumption $ G_2 $, which is weaker than the Doebli...
AbstractThis paper studies the asymptotic properties of the kernel probability density estimate of s...
This paper studies the asymptotic properties of the kernel probability density estimate of stationar...
Martingale theory is used to obtain a central limit theorem for degenerate U-statistics with variabl...
The nonparametric estimation results for time series described in the literature to date stem fairly...
In this paper, we study the problem of the nonparametric estimation of the marginal density f of a c...
AbstractIn this paper, we study the problem of the nonparametric estimation of the marginal density ...
AbstractIn order to construct confidence sets for a marginal density f of a strictly stationary cont...
AbstractA general nonparametric density estimation problem is considered in which the data is genera...
It has been shown recently that, under an appropriate integra-bility condition, densities of functio...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
The asymptotic behavior of nonparametric estimators of the probability density function of an i.i.d....
The Gaussian kernel density estimator is known to have substantial problems for bounded random varia...
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