This article introduces free-knot regression spline estimators for the mean and the variance components of a sample of curves. The asymptotic distribution of the mean estimator is derived, and asymptotic confidence bands are constructed. A comparative simulation study shows that free-knot splines estimate salient features of the functions (such as sharp peaks) more accurately than smoothing splines. This adaptive behavior is also illustrated by an analysis of weather data. Key words and phrases: Functional data analysis; Karhunen—Loève decomposi-tion; Longitudinal data analysis; Variance components
The sample observations of a functional variable are functions that come from the observation of a ...
In this paper we introduce a new method for automatically selecting knots in spline regression. The ...
Flexible data regression is an important tool for capturing complicated trends in data. One approach...
The paper considers functional linear regression, where scalar re- sponses are modeled in dependenc...
Data in many experiments arise as curves and therefore it is natural to use a curve as a basic unit ...
Abstract: Functional data analysis has received considerable recent attention and a number of succes...
This thesis provides a survey study on applications of spline functions to statistics. We start with...
We consider functional linear regression where a real variable Y depends on a func-tional variable X...
Department Head: F. Jay Breidt.2010 Spring.Includes bibliographical references (pages [74]-78).Nonpa...
We propose a global smoothing method based on polynomial splines for the estimation of functional co...
We propose a global smoothing method based on polynomial splines for the estimation of functional co...
ABSTRACT. We propose a global smoothing method based on polynomial splines for the es-timation of fu...
B-splines constitute an appealing method for the nonparametric estimation of a range of statis-tical...
Brumback and Rice are to be congratulated for this neat and excellent paper on the smoothing spline ...
We study the class of penalized spline estimators, which enjoy similarities to both regression splin...
The sample observations of a functional variable are functions that come from the observation of a ...
In this paper we introduce a new method for automatically selecting knots in spline regression. The ...
Flexible data regression is an important tool for capturing complicated trends in data. One approach...
The paper considers functional linear regression, where scalar re- sponses are modeled in dependenc...
Data in many experiments arise as curves and therefore it is natural to use a curve as a basic unit ...
Abstract: Functional data analysis has received considerable recent attention and a number of succes...
This thesis provides a survey study on applications of spline functions to statistics. We start with...
We consider functional linear regression where a real variable Y depends on a func-tional variable X...
Department Head: F. Jay Breidt.2010 Spring.Includes bibliographical references (pages [74]-78).Nonpa...
We propose a global smoothing method based on polynomial splines for the estimation of functional co...
We propose a global smoothing method based on polynomial splines for the estimation of functional co...
ABSTRACT. We propose a global smoothing method based on polynomial splines for the es-timation of fu...
B-splines constitute an appealing method for the nonparametric estimation of a range of statis-tical...
Brumback and Rice are to be congratulated for this neat and excellent paper on the smoothing spline ...
We study the class of penalized spline estimators, which enjoy similarities to both regression splin...
The sample observations of a functional variable are functions that come from the observation of a ...
In this paper we introduce a new method for automatically selecting knots in spline regression. The ...
Flexible data regression is an important tool for capturing complicated trends in data. One approach...