In this paper, we consider nonparametric estimation for dependent data, where the observations do not necessarily come from a linear process.We study density estimation and also discuss associated problems in nonparametric regression, using the 2-mixing dependence measure. We compare the results under the 2-mixing with those derived under the assumption that the process is linear
In this doctoral dissertation we will investigate dependence structures in three different cases. ...
A fundamental problem in statistics is the estimation of dependence between random variables. While ...
This dissertation consists of three essays related to the estimation of econometric models with depe...
We investigate nonparametric curve estimation (including density, distribution, hazard, conditional ...
AbstractWe investigate nonparametric curve estimation (including density, distribution, hazard, cond...
AbstractIn this paper a method for obtaining a.s. consistency in nonparametric estimation is present...
We consider the problem of multivariate density estimation, using samples from the distribution of i...
We consider the nonparametric estimation of the regression functions for dependentdata. Suppose that...
In this paper a method for obtaining a.s. consistency in nonparametric estimation is presented which...
Abstract: We consider the nonparametric estimation of the density func-tion of weakly and strongly d...
An approach to modeling dependent nonparametric random density functions is presented. This is based...
In the study of random processes, dependence is the rule rather than the exception. To facilitate th...
We provide a convenient econometric framework for the analysis of nonlinear dependence in financial ...
Motivated by causal inference problems, we propose a novel method for regression that minimizes the ...
Abstract. In this tutorial paper we give an overview of deconvolution problems in nonparametric stat...
In this doctoral dissertation we will investigate dependence structures in three different cases. ...
A fundamental problem in statistics is the estimation of dependence between random variables. While ...
This dissertation consists of three essays related to the estimation of econometric models with depe...
We investigate nonparametric curve estimation (including density, distribution, hazard, conditional ...
AbstractWe investigate nonparametric curve estimation (including density, distribution, hazard, cond...
AbstractIn this paper a method for obtaining a.s. consistency in nonparametric estimation is present...
We consider the problem of multivariate density estimation, using samples from the distribution of i...
We consider the nonparametric estimation of the regression functions for dependentdata. Suppose that...
In this paper a method for obtaining a.s. consistency in nonparametric estimation is presented which...
Abstract: We consider the nonparametric estimation of the density func-tion of weakly and strongly d...
An approach to modeling dependent nonparametric random density functions is presented. This is based...
In the study of random processes, dependence is the rule rather than the exception. To facilitate th...
We provide a convenient econometric framework for the analysis of nonlinear dependence in financial ...
Motivated by causal inference problems, we propose a novel method for regression that minimizes the ...
Abstract. In this tutorial paper we give an overview of deconvolution problems in nonparametric stat...
In this doctoral dissertation we will investigate dependence structures in three different cases. ...
A fundamental problem in statistics is the estimation of dependence between random variables. While ...
This dissertation consists of three essays related to the estimation of econometric models with depe...
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