summary:The problem of estimation of distribution functions or fractiles of non- negative random variables often occurs in the tasks of risk evaluation. There are many parametric models, however sometimes we need to know also some information about the shape and the type of the distribution. Unfortunately, classical approaches based on kernel approximations with a symmetric kernel do not give any guarantee of non-negativity for the low number of observations. In this note a heuristic approach, based on the assumption that non-negative distributions can be also approximated by means of kernels which are defined only on the positive real numbers, is discussed
Commonly used kernel density estimators may not provide admissible values of the density or its func...
The estimation of an unknown probability density functions of a random variable or its distribution ...
The aim of this thesis is to provide two extensions to the theory of nonparametric kernel density e...
summary:The problem of estimation of distribution functions or fractiles of non- negative random var...
summary:The problem of estimation of distribution functions or fractiles of non- negative random var...
We propose a new type of non parametric density estimators fitted to nonnegative random variables. T...
Testing the distribution of a random sample can be considered ,indeed, as a goodness-of-fit problem....
We propose kernel type estimators for the density function of non negative random variables, where t...
International audienceIn statistics, it is usually difficult to estimate the probability density fun...
This paper introduces two new nonparametric estimators for probability density functions which have ...
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...
The methods of nonparametric statistics are very useful in data analysis. One of the most popular me...
The negative binomial distribution (NBD) and negative binomial processes have been used as natural m...
Abstract. We propose a new type of non parametric density estimators fitted to nonnegative random va...
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is...
Commonly used kernel density estimators may not provide admissible values of the density or its func...
The estimation of an unknown probability density functions of a random variable or its distribution ...
The aim of this thesis is to provide two extensions to the theory of nonparametric kernel density e...
summary:The problem of estimation of distribution functions or fractiles of non- negative random var...
summary:The problem of estimation of distribution functions or fractiles of non- negative random var...
We propose a new type of non parametric density estimators fitted to nonnegative random variables. T...
Testing the distribution of a random sample can be considered ,indeed, as a goodness-of-fit problem....
We propose kernel type estimators for the density function of non negative random variables, where t...
International audienceIn statistics, it is usually difficult to estimate the probability density fun...
This paper introduces two new nonparametric estimators for probability density functions which have ...
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...
The methods of nonparametric statistics are very useful in data analysis. One of the most popular me...
The negative binomial distribution (NBD) and negative binomial processes have been used as natural m...
Abstract. We propose a new type of non parametric density estimators fitted to nonnegative random va...
In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is...
Commonly used kernel density estimators may not provide admissible values of the density or its func...
The estimation of an unknown probability density functions of a random variable or its distribution ...
The aim of this thesis is to provide two extensions to the theory of nonparametric kernel density e...