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Statistical Methodology

Selected Works

Philip T. Reiss

Articles 1 - 3 of 3

Full-Text Articles in Statistical Models

Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua Dec 2016

Penalized Nonparametric Scalar-On-Function Regression Via Principal Coordinates, Philip T. Reiss, David L. Miller, Pei-Shien Wu, Wen-Yu Hua

Philip T. Reiss

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. The core idea is to regress the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, the proposed …

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Flexible Penalized Regression For Functional Data...And Other Complex Data Objects, Philip T. Reiss Oct 2015

Flexible Penalized Regression For Functional Data...And Other Complex Data Objects, Philip T. Reiss

Philip T. Reiss

No abstract provided.

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Fast Function-On-Scalar Regression With Penalized Basis Expansions, Philip T. Reiss, Lei Huang, Maarten Mennes Dec 2009

Fast Function-On-Scalar Regression With Penalized Basis Expansions, Philip T. Reiss, Lei Huang, Maarten Mennes

Philip T. Reiss

Regression models for functional responses and scalar predictors are often fitted by means of basis functions, with quadratic roughness penalties applied to avoid overfitting. The fitting approach described by Ramsay and Silverman in the 1990s amounts to a penalized ordinary least squares (P-OLS) estimator of the coefficient functions. We recast this estimator as a generalized ridge regression estimator, and present a penalized generalized least squares (P-GLS) alternative. We describe algorithms by which both estimators can be implemented, with automatic selection of optimal smoothing parameters, in a more computationally efficient manner than has heretofore been available. We discuss pointwise confidence intervals …

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