In this paper, we introduce a new smooth estimator for continuous distribution functions on the positive real half-line using Szasz-Mirakyan operators, similar to Bernstein's approximation theorem. We show that the proposed estimator outperforms the empirical distribution function in terms of asymptotic (integrated) mean-squared error, and generally compares favourably with other competitors in theoretical comparisons. Also, we conduct the simulations to demonstrate the finite sample performance of the proposed estimator.Comment: Small typo in Theorem 10: Now -1/12 instead of +1/12 in the term of order $m^{-1}
Many asymptotic results for kernel-based estimators were established under some smoothness assumptio...
AbstractIn this article, we consider the problem of estimating a p-variate (p ≥ 3) normal mean vecto...
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...
Nonparametric maximum likelihood estimators (MLEs) in inverse problems often have non-normal limit d...
The Sz\'asz-Mirakyan operator is known as a positive linear operator which uniformly approximates a ...
Abstract. In [ 5] we have announced a h e a r spllne method for nonparametric density and distribut...
We introduce a version of Stein's method of comparison of operators specifically tailored to the pro...
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is...
The paper considers nonparametric estimation of absolutely continuous distribution functions of inde...
Commonly used kernel density estimators may not provide admissible values of the density or its func...
Click on the DOI link to access the article (may not be free).A lifetime X with survival function S,...
We derive product limit estimators of survival times and failure rates for randomly right censored d...
In Mombeni et al. (2019), Birnbaum-Saunders and Weibull kernel estimators were introduced for the es...
Let X and Y be two random variables denoting life times having finite means. Let, S 1 , S 2 and M 1 ...
In this research, several approximation of the probability density function, cumulative distributio...
Many asymptotic results for kernel-based estimators were established under some smoothness assumptio...
AbstractIn this article, we consider the problem of estimating a p-variate (p ≥ 3) normal mean vecto...
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...
Nonparametric maximum likelihood estimators (MLEs) in inverse problems often have non-normal limit d...
The Sz\'asz-Mirakyan operator is known as a positive linear operator which uniformly approximates a ...
Abstract. In [ 5] we have announced a h e a r spllne method for nonparametric density and distribut...
We introduce a version of Stein's method of comparison of operators specifically tailored to the pro...
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is...
The paper considers nonparametric estimation of absolutely continuous distribution functions of inde...
Commonly used kernel density estimators may not provide admissible values of the density or its func...
Click on the DOI link to access the article (may not be free).A lifetime X with survival function S,...
We derive product limit estimators of survival times and failure rates for randomly right censored d...
In Mombeni et al. (2019), Birnbaum-Saunders and Weibull kernel estimators were introduced for the es...
Let X and Y be two random variables denoting life times having finite means. Let, S 1 , S 2 and M 1 ...
In this research, several approximation of the probability density function, cumulative distributio...
Many asymptotic results for kernel-based estimators were established under some smoothness assumptio...
AbstractIn this article, we consider the problem of estimating a p-variate (p ≥ 3) normal mean vecto...
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...