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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 …
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 …