Penalized regression with individual deviance effects

A Perperoglou, Paul Eilers

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Abstract

The present work addresses the problem of model estimation and computations for discrete data when some covariates are modeled smoothly using splines. We propose to introduce and explicitly estimate individual deviance effects (one for each observation), constrained by a ridge penalty. This turns out to be an effective way to absorb model excess variation and detect systematic patterns. Large but very sparse systems of penalized likelihood equations have to be solved. We present fast and compact algorithms for fitting, estimation and computation of the effective dimension. Applications to counts, binomial, and survival data illustrate practical use of this model.
Original languageUndefined/Unknown
Pages (from-to)341-361
Number of pages21
JournalComputational Statistics
Volume25
Issue number2
DOIs
Publication statusPublished - 2010

Research programs

  • EMC NIHES-01-66-01

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