An overview on variable selection for longitudinal data

Research output: Chapter in Book/Report/Conference proceedingChapter

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Abstract

During the past two decades, there have been many new developments in longitudinal data analysis. Authors have made many efforts on devel- oping diverse models, along with inference procedures, for longitudinal data. More recently, researchers in longitudinal modeling have begun ad- dressing the vital issue of variable selection. Model selection criteria such as AIC, BIC, Cp, LASSO and SCAD can be extended to longitudinal data, although care is required to adapt the classical ideas and formulas to deal with within-subject correlation. This chapter presents a review on recent developments on variable selection criteria for longitudinal data.

Original languageEnglish (US)
Title of host publicationQuantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques
PublisherWorld Scientific Publishing Co.
Pages3-24
Number of pages22
ISBN (Electronic)9789812772121
ISBN (Print)9812704612, 9789812704610
DOIs
StatePublished - Jan 1 2007

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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    Dziak, J. J., & Li, R. (2007). An overview on variable selection for longitudinal data. In Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques (pp. 3-24). World Scientific Publishing Co.. https://doi.org/10.1142/9789812772121_0001