A generalized functional linear regression model is proposed by considering a functional covariate and its derivatives as functional predictors. The unobserved derivatives of a random function may carry useful information and need to be estimated. We apply the notion of functional principal component analysis to modeling functional predictors. The proposed regression model is parameterized in various ways to investigate the effect of each functional predictor. The performance of the proposed method is demonstrated through a traffic data example.補正完
The local linear regression technique is applied to estimation of functional-coefficient regression ...
The aim of this thesis is to systematically investigate some functional regression models for accura...
We study regression models for the situation where both dependent and independent variables are squa...
Summary: Functional principal component regression (FPCR) is a promising new method for regressing s...
AbstractWe analyze in a regression setting the link between a scalar response and a functional predi...
We analyze in a regression setting the link between a scalar response and a functional predictor by ...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
Nowadays, with data collecting methods developing rapidly, effectively functional type responses are...
Functional data occurs when we observe curves or paths from a stochastic process Xt. If for each cur...
This note deals with the study of a functional linear model for time series prediction which combine...
We introduce a new model of linear regression for random func-tional inputs taking into account the ...
With the advance of modern technology, more and more data are being recorded continuously during a t...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
We study sequential monitoring procedures that detect instabilities of the regression operator in an...
We introduce a new class of functional generalized linear models, where the response is a scalar and...
The local linear regression technique is applied to estimation of functional-coefficient regression ...
The aim of this thesis is to systematically investigate some functional regression models for accura...
We study regression models for the situation where both dependent and independent variables are squa...
Summary: Functional principal component regression (FPCR) is a promising new method for regressing s...
AbstractWe analyze in a regression setting the link between a scalar response and a functional predi...
We analyze in a regression setting the link between a scalar response and a functional predictor by ...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
Nowadays, with data collecting methods developing rapidly, effectively functional type responses are...
Functional data occurs when we observe curves or paths from a stochastic process Xt. If for each cur...
This note deals with the study of a functional linear model for time series prediction which combine...
We introduce a new model of linear regression for random func-tional inputs taking into account the ...
With the advance of modern technology, more and more data are being recorded continuously during a t...
In this paper, we study a regression model in which explanatory variables are sampling points of a c...
We study sequential monitoring procedures that detect instabilities of the regression operator in an...
We introduce a new class of functional generalized linear models, where the response is a scalar and...
The local linear regression technique is applied to estimation of functional-coefficient regression ...
The aim of this thesis is to systematically investigate some functional regression models for accura...
We study regression models for the situation where both dependent and independent variables are squa...