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
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...
This thesis contains several nonparametric estimation procedures of a probability density function.I...
We introduce a new, flexible family of distributions for non-negative data, defined by means of a qu...
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...
This paper introduces two new nonparametric estimators for probability density functions which have ...
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...
Abstract. We propose a new type of non parametric density estimators fitted to nonnegative random va...
Abstract. Together with the dynamic development of modern computer systems, the possibilities of app...
New nonparametric procedure for estimating the probability density function of a positive random var...
We present estimators for nonparametric functions that depend on unobservable random vari-ables in n...
We propose a class of unbiased and strongly consistent nonparametric kernel estimates of a probabili...
Standard fixed symmetric kernel type density estimators are known to encounter problems for positive...
Cette thèse comporte plusieurs procédures d'estimation non-paramétrique de densité de probabilité.Da...
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...
This thesis contains several nonparametric estimation procedures of a probability density function.I...
We introduce a new, flexible family of distributions for non-negative data, defined by means of a qu...
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...
This paper introduces two new nonparametric estimators for probability density functions which have ...
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...
Abstract. We propose a new type of non parametric density estimators fitted to nonnegative random va...
Abstract. Together with the dynamic development of modern computer systems, the possibilities of app...
New nonparametric procedure for estimating the probability density function of a positive random var...
We present estimators for nonparametric functions that depend on unobservable random vari-ables in n...
We propose a class of unbiased and strongly consistent nonparametric kernel estimates of a probabili...
Standard fixed symmetric kernel type density estimators are known to encounter problems for positive...
Cette thèse comporte plusieurs procédures d'estimation non-paramétrique de densité de probabilité.Da...
In this lecture, we discuss kernel estimation of probability density functions (PDF). Nonparametric ...
This thesis contains several nonparametric estimation procedures of a probability density function.I...
We introduce a new, flexible family of distributions for non-negative data, defined by means of a qu...