Add to ORKGIn this article a new nonparametric density estimator based on the sequence of asymmetric kernels is proposed. This method is natural when estimating an unknown density function of a positive random variable. The rates of Mean Squared Error, Mean Integrated Squared Error, and the L 1-consistency are investigated. Simulation studies are conducted to compare a new estimator and its modified version with traditional kernel density construction.CC999999/Intramural CDC HHS/United States2016-01-04T00:00:00Z26740729PMC469944
Commonly used kernel density estimators may not provide admissible values of the density or its func...
Abstract. We propose a new type of non parametric density estimators fitted to nonnegative random va...
The kernel distribution function estimator method is the most popular nonparametric method to estima...
New nonparametric procedure for estimating the probability density function of a positive random var...
This article describes asciker and bsciker, two programs that enrich the possibility for density an...
Standard fixed symmetric kernel type density estimators are known to encounter problems for positive...
This article considers smooth density estimation based on length biased data that involves a random ...
We propose kernel type estimators for the density function of non negative random variables, where t...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
We introduce a new class of nonparametric density estimators. It includes the classical kernel densi...
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...
Kernel density estimators (KDEs) are ubiquitous tools for nonparametric estimation of probability de...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
We propose a new type of non parametric density estimators fitted to nonnegative random variables. T...
In this article we propose two new Multiplicative Bias Correction (MBC) techniques for nonparametric...
Commonly used kernel density estimators may not provide admissible values of the density or its func...
Abstract. We propose a new type of non parametric density estimators fitted to nonnegative random va...
The kernel distribution function estimator method is the most popular nonparametric method to estima...
New nonparametric procedure for estimating the probability density function of a positive random var...
This article describes asciker and bsciker, two programs that enrich the possibility for density an...
Standard fixed symmetric kernel type density estimators are known to encounter problems for positive...
This article considers smooth density estimation based on length biased data that involves a random ...
We propose kernel type estimators for the density function of non negative random variables, where t...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
We introduce a new class of nonparametric density estimators. It includes the classical kernel densi...
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...
Kernel density estimators (KDEs) are ubiquitous tools for nonparametric estimation of probability de...
We consider semiparametric asymmetric kernel density estimators when the unknown density has support...
We propose a new type of non parametric density estimators fitted to nonnegative random variables. T...
In this article we propose two new Multiplicative Bias Correction (MBC) techniques for nonparametric...
Commonly used kernel density estimators may not provide admissible values of the density or its func...
Abstract. We propose a new type of non parametric density estimators fitted to nonnegative random va...
The kernel distribution function estimator method is the most popular nonparametric method to estima...