Add to ORKGA non-parametric method of distribution estimation for univariate data is presented. The idea is to adapt the smoothing spline procedure used in regression to the estimation of distributions via a scatterplot smoothing of theempirical distribution function. An explicit formula for the estimator is obtained by minimizing a penalized weighted sum of squares. The issue of monotonicity of the resulting function is discussed in detail and the estimator's large sample properties are studied
This paper describes a new method of monotone interpolation and smoothing of multivariate scattered ...
In this paper a new estimator for nonparametric regression is suggested. It is a smoothing-splines-l...
There are two popular smoothing parameter selection methods for spline smoothing. First, smoothing p...
Estimation of growth curves or item response curves often involves monotone data smoothing. Methods ...
The estimation of cumulative distributions is classically performed using the empirical distribution...
The scope is to smooth the empirical distribution function of a random sample by minimizing a penali...
In recent years there has been an increasing interest in the study of interpolation procedures prese...
In some regression settings one would like to combine the flexibility of nonparametric smoothing wit...
<div><p>Various methods have been proposed for smoothing under the monotonicity constraint. We revie...
In some applications, we require a monotone estimate of a regression function. In others, we want to...
In this thesis we address the problem of estimating a curve of interest (which might be a probabilit...
AbstractWe study a spline-based likelihood method for the partly linear model with monotonicity cons...
Density estimation plays a fundamental role in many areas including statistics and machine learning....
Estimation of regression functions from bounded, independent and identically distributed data is con...
Abstract. In [ 5] we have announced a h e a r spllne method for nonparametric density and distribut...
This paper describes a new method of monotone interpolation and smoothing of multivariate scattered ...
In this paper a new estimator for nonparametric regression is suggested. It is a smoothing-splines-l...
There are two popular smoothing parameter selection methods for spline smoothing. First, smoothing p...
Estimation of growth curves or item response curves often involves monotone data smoothing. Methods ...
The estimation of cumulative distributions is classically performed using the empirical distribution...
The scope is to smooth the empirical distribution function of a random sample by minimizing a penali...
In recent years there has been an increasing interest in the study of interpolation procedures prese...
In some regression settings one would like to combine the flexibility of nonparametric smoothing wit...
<div><p>Various methods have been proposed for smoothing under the monotonicity constraint. We revie...
In some applications, we require a monotone estimate of a regression function. In others, we want to...
In this thesis we address the problem of estimating a curve of interest (which might be a probabilit...
AbstractWe study a spline-based likelihood method for the partly linear model with monotonicity cons...
Density estimation plays a fundamental role in many areas including statistics and machine learning....
Estimation of regression functions from bounded, independent and identically distributed data is con...
Abstract. In [ 5] we have announced a h e a r spllne method for nonparametric density and distribut...
This paper describes a new method of monotone interpolation and smoothing of multivariate scattered ...
In this paper a new estimator for nonparametric regression is suggested. It is a smoothing-splines-l...
There are two popular smoothing parameter selection methods for spline smoothing. First, smoothing p...