Add to ORKGThis manuscript discusses the strong consistency and the asymptotic distribution of an estimator for a periodic component of the intensity function having a form of periodic function multiplied by power function trend of a non-homogeneous Poisson process by using a uniform kernel function. It is assumed that the period of the periodic component of intensity function is known. An estimator for the periodic component using only a single realization of a Poisson process observed at a certain interval has been constructed. This estimator has been proved to be strongly consistent if the length of the observation interval indefinitely expands. Computer simulation also showed the asymptotic normality of this estimato
AbstractWe consider a kernel-type nonparametric estimator of the intensity function of a cyclic Pois...
In this manuscript, estimation of the periodic component of intensity having form periodic function ...
Motivated by applications of Poisson processes for modelling periodic time-varying phenomena, we stu...
This manuscript discusses the strong consistency and the asymptotic distribution of an estimator for...
Abstract: Problem statement: In this study, we construct the estimation for a periodic component of ...
Abstract. A uniform kernel estimator for intensity of a periodic Poisson process with unknowm period...
A uniform kernel estimator for intensity of a periodic Poisson process with unknowm period is presen...
AbstractWe construct and investigate a consistent kernel-type nonparametric estimator of the intensi...
We construct and investigate a consistent kernel-type nonparametric estimator of the intensity funct...
In this article, we provided a numerical simulation for asymptotic normality of a kernel type estima...
In this paper we prove asymptotic normality of a kernel type estimator for the intensity of a period...
In this paper, an asymptotic distribution of the estimator for the variance function of a compound p...
We consider a kernel-type nonparametric estimator of the intensity function of a cyclic Poisson proc...
An estimator of the intensity in the form of a power function of an inhomogeneous Poisson process is...
In a series of papers, J. Garrido and Y. Lu have proposed and investigated a doubly-periodic Poisson...
AbstractWe consider a kernel-type nonparametric estimator of the intensity function of a cyclic Pois...
In this manuscript, estimation of the periodic component of intensity having form periodic function ...
Motivated by applications of Poisson processes for modelling periodic time-varying phenomena, we stu...
This manuscript discusses the strong consistency and the asymptotic distribution of an estimator for...
Abstract: Problem statement: In this study, we construct the estimation for a periodic component of ...
Abstract. A uniform kernel estimator for intensity of a periodic Poisson process with unknowm period...
A uniform kernel estimator for intensity of a periodic Poisson process with unknowm period is presen...
AbstractWe construct and investigate a consistent kernel-type nonparametric estimator of the intensi...
We construct and investigate a consistent kernel-type nonparametric estimator of the intensity funct...
In this article, we provided a numerical simulation for asymptotic normality of a kernel type estima...
In this paper we prove asymptotic normality of a kernel type estimator for the intensity of a period...
In this paper, an asymptotic distribution of the estimator for the variance function of a compound p...
We consider a kernel-type nonparametric estimator of the intensity function of a cyclic Poisson proc...
An estimator of the intensity in the form of a power function of an inhomogeneous Poisson process is...
In a series of papers, J. Garrido and Y. Lu have proposed and investigated a doubly-periodic Poisson...
AbstractWe consider a kernel-type nonparametric estimator of the intensity function of a cyclic Pois...
In this manuscript, estimation of the periodic component of intensity having form periodic function ...
Motivated by applications of Poisson processes for modelling periodic time-varying phenomena, we stu...