Add to ORKGAn algorithm for an approximating function to the frequency distribution is obtained from a sample of size n. To obtain the approximating function a histogram is made from the data. Next, Euclidean space approximations to the graph of the histogram using central B-splines as basis elements are obtained by linear programming. The approximating function has area one and is nonnegative
Abstract—Starting from the power spectral density of Matérn stochastic processes, we introduce a new...
This dissertation develops a uni ed framework to study the (asymptotic) properties of all (periodic)...
The Hybrid Spline method (H-spline) is a method of density estimation which involves regression spli...
Two algorithms are presented for smoothing arbitrary sets of data. They are the explicit variable al...
This paper deals with the problem of the approximation of a density function underlying a given hist...
AbstractThis paper presents an algorithm for fitting a smoothing spline function to a set of experim...
The thrust of this report concerns spline theory and some of the background to spline theory and fol...
This thesis provides a survey study on applications of spline functions to statistics. We start with...
We present a new method for reconstructing the density function underlying a given histogram. First...
We consider the problem of estimating a smoothing spline where the penalty on the smoothing function...
A succinct introduction to splines, explaining how and why B-splines are used as a basis and how cub...
L'approximation de fonctions et de données discrètes est fondamentale dans des domaines tels que la ...
Finding low-dimensional approximations to high-dimensional data is one of the most important topics ...
Splines, which were invented by Schoenberg more than fifty years ago [1], constitute an elegant fram...
When tuning the smoothness parameter of nonparametric regression splines, the evaluation of the so-c...
Abstract—Starting from the power spectral density of Matérn stochastic processes, we introduce a new...
This dissertation develops a uni ed framework to study the (asymptotic) properties of all (periodic)...
The Hybrid Spline method (H-spline) is a method of density estimation which involves regression spli...
Two algorithms are presented for smoothing arbitrary sets of data. They are the explicit variable al...
This paper deals with the problem of the approximation of a density function underlying a given hist...
AbstractThis paper presents an algorithm for fitting a smoothing spline function to a set of experim...
The thrust of this report concerns spline theory and some of the background to spline theory and fol...
This thesis provides a survey study on applications of spline functions to statistics. We start with...
We present a new method for reconstructing the density function underlying a given histogram. First...
We consider the problem of estimating a smoothing spline where the penalty on the smoothing function...
A succinct introduction to splines, explaining how and why B-splines are used as a basis and how cub...
L'approximation de fonctions et de données discrètes est fondamentale dans des domaines tels que la ...
Finding low-dimensional approximations to high-dimensional data is one of the most important topics ...
Splines, which were invented by Schoenberg more than fifty years ago [1], constitute an elegant fram...
When tuning the smoothness parameter of nonparametric regression splines, the evaluation of the so-c...
Abstract—Starting from the power spectral density of Matérn stochastic processes, we introduce a new...
This dissertation develops a uni ed framework to study the (asymptotic) properties of all (periodic)...
The Hybrid Spline method (H-spline) is a method of density estimation which involves regression spli...