Add to ORKGDensity Estimation, particularly the procedure using Kernel Functions, is fast becoming a crucial area of investigation in non-parametric statistics. This paper presents a computer program which empirically produces estimates of probability density functions using Kernel functions. It also attempts to evaluate the discrepancy of the estimates, as well as to collate observations on the resulting estimates, using as reference the limited samples generated and particular Kernel functions
Abstract. We consider kernel density estimation in the multivariate case, focusing on the use of som...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
In the field of data analysis, including environmental data, it is important to know the shape of un...
Density Estimation, particularly the procedure using Kernel Functions, is fast becoming a crucial ar...
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...
There are various methods for estimating a density. A group of methods which estimate the density as...
Histograms are the usual vehicle for representing medium sized data distributions graphically, but t...
Histograms are a useful but limited way to estimate or visualize the true, underlying density of som...
Histograms are a useful but limited way to estimate or visualize the true, underlying density of som...
The specification, based on experimental data, of functions which characterize an ob-ject under inve...
We propose kernel type estimators for the density function of non negative random variables, where t...
Abstract. We consider kernel density estimation in the multivariate case, focusing on the use of som...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
Abstract. We consider kernel density estimation in the multivariate case, focusing on the use of som...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
In the field of data analysis, including environmental data, it is important to know the shape of un...
Density Estimation, particularly the procedure using Kernel Functions, is fast becoming a crucial ar...
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...
There are various methods for estimating a density. A group of methods which estimate the density as...
Histograms are the usual vehicle for representing medium sized data distributions graphically, but t...
Histograms are a useful but limited way to estimate or visualize the true, underlying density of som...
Histograms are a useful but limited way to estimate or visualize the true, underlying density of som...
The specification, based on experimental data, of functions which characterize an ob-ject under inve...
We propose kernel type estimators for the density function of non negative random variables, where t...
Abstract. We consider kernel density estimation in the multivariate case, focusing on the use of som...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
Abstract. We consider kernel density estimation in the multivariate case, focusing on the use of som...
We show that maximum likelihood weighted kernel density estimation offers a unified approach to dens...
In the field of data analysis, including environmental data, it is important to know the shape of un...