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Medical Biomathematics and Biometrics Commons™
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- B-splines (3)
- Functional connectivity (2)
- Functional linear model (2)
- Functional principal component regression (2)
- Linear mixed model (2)
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- Smoothing parameter (2)
- Adjusted p-values (1)
- Analysis of distance (1)
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- Attention deficit/hyperactivity disorder (1)
- Bivariate smoothing (1)
- Bray-Curtis distance (1)
- Cortical thickness (1)
- Developmental trajectory (1)
- False discovery rate (1)
- Fractional anisotropy (1)
- Function-on-scalar regression (1)
- Functional data analysis (1)
- Functional magnetic resonance imaging (1)
- Functional principal components (1)
- Generalized approximate cross-validation (1)
- Generalized cross-validation (1)
- Growth chart (1)
- Homotopic connectivity (1)
- Massively parallel nonparametrics (1)
- Model selection (1)
- Multiple testing (1)
- Multiresponse permutation procedure (1)
- Multivariate analysis of variance (1)
- Multivariate calibration (1)
Articles 1 - 20 of 20
Full-Text Articles in Medical Biomathematics and Biometrics
Varying-Smoother Models For Functional Responses, Philip T. Reiss, Lei Huang, Huaihou Chen, Stan Colcombe
Varying-Smoother Models For Functional Responses, Philip T. Reiss, Lei Huang, Huaihou Chen, Stan Colcombe
Philip T. Reiss
This paper studies estimation of a smooth function f(x,v) when we are given functional responses of the form f(x, ·) + error, but scientific interest centers on the collection of functions f(·,v) for different v. The motivation comes from studies of human brain development, in which x denotes age whereas v refers to brain locations. Analogously to varying-coefficient models, in which the mean response is linear in x, the “varying-smoother” models that we consider exhibit nonlinear dependence on x that varies smoothly with v. We discuss three approaches to estimating varying-smoother models: (a) methods that employ a tensor product penalty; …
Paradoxical Results Of Adaptive False Discovery Rate Procedures In Neuroimaging Studies, Philip T. Reiss, Armin Schwartzman, Feihan Lu, Lei Huang, Erika Proal
Paradoxical Results Of Adaptive False Discovery Rate Procedures In Neuroimaging Studies, Philip T. Reiss, Armin Schwartzman, Feihan Lu, Lei Huang, Erika Proal
Philip T. Reiss
Adaptive false discovery rate (FDR) procedures, which offer greater power than the original FDR procedure of Benjamini and Hochberg, are often applied to statistical maps of the brain. When a large proportion of the null hypotheses are false, as in the case of widespread effects such as cortical thinning throughout much of the brain, adaptive FDR methods can surprisingly reject more null hypotheses than not accounting for multiple testing at all—i.e., using uncorrected p-values. A straightforward mathematical argument is presented to explain why this can occur with the q-value method of Storey and colleagues, and a simulation study shows that …
Function-On-Scalar Regression With The Refund Package, Philip T. Reiss
Function-On-Scalar Regression With The Refund Package, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Smoothness Selection For Penalized Quantile Regression Splines, Philip T. Reiss, Lei Huang
Smoothness Selection For Penalized Quantile Regression Splines, Philip T. Reiss, Lei Huang
Philip T. Reiss
Modern data-rich analyses may call for fitting a large number of nonparametric quantile regressions. For example, growth charts may be constructed for each of a collection of variables, to identify those for which individuals with a disorder tend to fall in the tails of their age-specific distribution; such variables might serve as developmental biomarkers. When such analyses are carried out by penalized spline smoothing, reliable automatic selection of the smoothing parameter is particularly important. We show that two popular methods for smoothness selection may tend to overfit when estimating extreme quantiles as a smooth function of a predictor such as …
Semiparametric Methods For Mapping Brain Development, Philip T. Reiss, Yin-Hsiu Chen, Lan Huo
Semiparametric Methods For Mapping Brain Development, Philip T. Reiss, Yin-Hsiu Chen, Lan Huo
Philip T. Reiss
No abstract provided.
Introducing Functional Data Analysis To Neuroimaging, And Vice Versa, Philip T. Reiss
Introducing Functional Data Analysis To Neuroimaging, And Vice Versa, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Massively Parallel Nonparametrics [Hds 2011 Slides], Philip T. Reiss, Lei Huang
Massively Parallel Nonparametrics [Hds 2011 Slides], Philip T. Reiss, Lei Huang
Philip T. Reiss
No abstract provided.
Flexible Dependence Of Functional Responses On Scalar Predictors, Philip T. Reiss, Lei Huang
Flexible Dependence Of Functional Responses On Scalar Predictors, Philip T. Reiss, Lei Huang
Philip T. Reiss
No abstract provided.
Extracting Information From Functional Connectivity Maps Via Function-On-Scalar Regression, Philip T. Reiss, Maarten Mennes, Eva Petkova, Lei Huang, Matthew J. Hoptman, Bharat B. Biswal, Stanley J. Colcombe, Xi-Nian Zuo, Michael P. Milham
Extracting Information From Functional Connectivity Maps Via Function-On-Scalar Regression, Philip T. Reiss, Maarten Mennes, Eva Petkova, Lei Huang, Matthew J. Hoptman, Bharat B. Biswal, Stanley J. Colcombe, Xi-Nian Zuo, Michael P. Milham
Philip T. Reiss
Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel's BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship …
Fast, Flexible Function-On-Scalar Regression, With An Application To Brain Development, Philip T. Reiss, Lei Huang
Fast, Flexible Function-On-Scalar Regression, With An Application To Brain Development, Philip T. Reiss, Lei Huang
Philip T. Reiss
No abstract provided.
On Distance-Based Permutation Tests For Between-Group Comparisons, Philip T. Reiss, M. Henry H. Stevens, Zarrar Shehzad, Eva Petkova, Michael P. Milham
On Distance-Based Permutation Tests For Between-Group Comparisons, Philip T. Reiss, M. Henry H. Stevens, Zarrar Shehzad, Eva Petkova, Michael P. Milham
Philip T. Reiss
Permutation tests based on distances among multivariate observations have found many applications in the biological sciences. Two major testing frameworks of this kind are multiresponse permutation procedures and pseudo-F tests arising from a distance-based extension of multivariate analysis of variance. In this paper we derive conditions under which these two frameworks are equivalent. The methods and equivalence results are illustrated by reanalyzing an ecological data set and by a novel application to functional magnetic resonance imaging data.
Functional Generalized Linear Models With Images As Predictors, Philip T. Reiss, R. Todd Ogden
Functional Generalized Linear Models With Images As Predictors, Philip T. Reiss, R. Todd Ogden
Philip T. Reiss
Functional principal component regression (FPCR) is a promising new method for regressing scalar outcomes on functional predictors. In this paper we present a theoretical justification 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 …
Regression When The Predictors Are Images, Philip T. Reiss
Regression When The Predictors Are Images, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Smoothing Parameter Selection For A Class Of Semiparametric Linear Models, Philip T. Reiss, R. Todd Ogden
Smoothing Parameter Selection For A Class Of Semiparametric Linear Models, Philip T. Reiss, R. Todd Ogden
Philip T. Reiss
Spline-based approaches to nonparametric and semiparametric regression, as well as to regression of scalar outcomes on functional predictors, entail choosing a parameter controlling the extent to which roughness of the fitted function is penalized. In this paper we demonstrate that the equations determining two popular methods for smoothing parameter selection, generalized cross-validation and restricted maximum likelihood, share a similar form that allows us to prove several results common to both, and to derive a condition under which they yield identical values. These ideas are illustrated by application of functional principal component regression, a method for regressing scalars on functions, to …
Regressing Scalar Outcomes On Image Predictors Via Functional Principal Component Regression, Philip T. Reiss
Regressing Scalar Outcomes On Image Predictors Via Functional Principal Component Regression, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Simultaneous Confidence Bands For The Coefficient Function In Functional Regression, Philip T. Reiss
Simultaneous Confidence Bands For The Coefficient Function In Functional Regression, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Inferring Group Differences In Brain Connectivity From Functional Magnetic Resonance Images, Philip T. Reiss
Inferring Group Differences In Brain Connectivity From Functional Magnetic Resonance Images, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Reliability Of Functional Connectivity Networks: How Can We Assess It?, Philip T. Reiss
Reliability Of Functional Connectivity Networks: How Can We Assess It?, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Functional Generalized Linear Models With Applications To Neuroimaging, Philip T. Reiss, R. Todd Ogden
Functional Generalized Linear Models With Applications To Neuroimaging, Philip T. Reiss, R. Todd Ogden
Philip T. Reiss
No abstract provided.
Functional Principal Component Regression And Functional Partial Least Squares, Philip T. Reiss, R. Todd Ogden
Functional Principal Component Regression And Functional Partial Least Squares, Philip T. Reiss, R. Todd Ogden
Philip T. Reiss
Regression of a scalar response on signal predictors, such as near-infrared (NIR) spectra of chemical samples, presents a major challenge when, as is typically the case, the dimension of the signals far exceeds their number. Most solutions to this problem reduce the dimension of the predictors either by regressing on components--e.g. principal component regression (PCR) and partial least squares (PLS)--or by smoothing methods which restrict the coefficient function to the span of a spline basis. This paper introduces functional versions of PCR and PLS, which combine both of the above dimension reduction approaches. Two versions of functional PCR are developed, …