Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 5 of 5
Full-Text Articles in Life Sciences
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.
Resampling-Based Information Criteria For Best-Subset Regression, Philip T. Reiss, Lei Huang, Joseph E. Cavanaugh, Amy Krain Roy
Resampling-Based Information Criteria For Best-Subset Regression, Philip T. Reiss, Lei Huang, Joseph E. Cavanaugh, Amy Krain Roy
Philip T. Reiss
When a linear model is chosen by searching for the best subset among a set of candidate predictors, a fixed penalty such as that imposed by the Akaike information criterion may penalize model complexity inadequately, leading to biased model selection. We study resampling-based information criteria that aim to overcome this problem through improved estimation of the effective model dimension. The first proposed approach builds upon previous work on bootstrap-based model selection. We then propose a more novel approach based on cross-validation. Simulations and analyses of a functional neuroimaging data set illustrate the strong performance of our resampling-based methods, which are …