There exist many ways to estimate the shape of the underlying density. Generally, we can categorize them into a parametric and a nonparametric methodology. Examples of a nonparametric density estimation are histogram and kernel density estimation. Another example of the nonparametric methodology is orthogonal series density estimation. In this work, we will describe the fundamental idea behind this methodology. We will also show how Kronmal-Tarter method estimates the density of known underlying data
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
Previously, we’ve assumed that the forms of the underlying densities were of some particular known p...
Tech ReportGiven a sample set X1,...,XN of independent identically distributed real-valued random va...
Orthogonal series estimators of univariate densities are proposed that are derived from and motivate...
International audienceIn statistics, it is usually difficult to estimate the probability density fun...
This paper concerns estimation of mixture densities. It is the continuation of the work of Pommeret ...
Abstract—The density estimates considered in this paper comprise a base density and an adjustment co...
The density estimates considered in this paper comprise a base density and an adjustment component c...
Kernel principal component analysis has been introduced as a method of extracting a set of orthonorm...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
In this work, three extensions of univariate nonparametric probability density estimators into two d...
The object of the present study is to summarize recent developments in nonparametric density estimat...
Previously, we’ve assumed that the forms of the underlying densities were of some particular known p...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
Previously, we’ve assumed that the forms of the underlying densities were of some particular known p...
Tech ReportGiven a sample set X1,...,XN of independent identically distributed real-valued random va...
Orthogonal series estimators of univariate densities are proposed that are derived from and motivate...
International audienceIn statistics, it is usually difficult to estimate the probability density fun...
This paper concerns estimation of mixture densities. It is the continuation of the work of Pommeret ...
Abstract—The density estimates considered in this paper comprise a base density and an adjustment co...
The density estimates considered in this paper comprise a base density and an adjustment component c...
Kernel principal component analysis has been introduced as a method of extracting a set of orthonorm...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
Kernel density estimation is a technique for estimation of probability density function that is a mu...
In this work, three extensions of univariate nonparametric probability density estimators into two d...
The object of the present study is to summarize recent developments in nonparametric density estimat...
Previously, we’ve assumed that the forms of the underlying densities were of some particular known p...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
Previously, we’ve assumed that the forms of the underlying densities were of some particular known p...
Tech ReportGiven a sample set X1,...,XN of independent identically distributed real-valued random va...