Add to ORKGSummary: Functional principal component regression (FPCR) is a promising new method for regressing scalar outcomes on functional predictors. In this paper we present a theoretical justifi-cation for the use of principal components in functional regression. FPCR is then extended in two directions: from linear to the generalized linear modeling, and from univariate signal predictors to high-resolution image predictors. We show how to implement the method efficiently by adapting generalized additive model technology to the functional regression context. A technique is proposed for estimating simultaneous confidence bands for the coefficient function; in the neuroimaging setting, this yields a novel means to identify brain regions that are asso...
We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to...
<div><p>The goal of our article is to provide a transparent, robust, and computationally feasible st...
Motivated by recent work on studying massive imaging data in various neuroimaging studies,our group ...
We introduce the functional generalized additive model (FGAM), a novel regression model for associat...
An important class of prediction problems in modern biomedical studies is to use medical images as w...
Functional data analysis (FDA) plays an important role in analyzing function-valued data such as gro...
<div><p>We introduce the functional generalized additive model (FGAM), a novel regression model for ...
A generalized functional linear regression model is proposed by considering a functional covariate a...
Principal Component Analysis (PCA) is one widely used data processing technique in application, espe...
The goal of our article is to provide a transparent, robust, and computationally feasible statistica...
Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in w...
This thesis delves into the world of Functional Data Analysis (FDA) and its analog of Principal Comp...
We develop fast fitting methods for generalized functional linear models. An undersmooth of the func...
<p>Existing approaches for multivariate functional principal component analysis are restricted to da...
Advances in data collection and storage have tremendously increased the presence of functional data,...
We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to...
<div><p>The goal of our article is to provide a transparent, robust, and computationally feasible st...
Motivated by recent work on studying massive imaging data in various neuroimaging studies,our group ...
We introduce the functional generalized additive model (FGAM), a novel regression model for associat...
An important class of prediction problems in modern biomedical studies is to use medical images as w...
Functional data analysis (FDA) plays an important role in analyzing function-valued data such as gro...
<div><p>We introduce the functional generalized additive model (FGAM), a novel regression model for ...
A generalized functional linear regression model is proposed by considering a functional covariate a...
Principal Component Analysis (PCA) is one widely used data processing technique in application, espe...
The goal of our article is to provide a transparent, robust, and computationally feasible statistica...
Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in w...
This thesis delves into the world of Functional Data Analysis (FDA) and its analog of Principal Comp...
We develop fast fitting methods for generalized functional linear models. An undersmooth of the func...
<p>Existing approaches for multivariate functional principal component analysis are restricted to da...
Advances in data collection and storage have tremendously increased the presence of functional data,...
We extend four tests common in classical regression - Wald, score, likelihood ratio and F tests - to...
<div><p>The goal of our article is to provide a transparent, robust, and computationally feasible st...
Motivated by recent work on studying massive imaging data in various neuroimaging studies,our group ...