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 language||English (US)|
|Title of host publication||Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques|
|Publisher||World Scientific Publishing Co.|
|Number of pages||22|
|ISBN (Print)||9812704612, 9789812704610|
|State||Published - Jan 1 2007|
All Science Journal Classification (ASJC) codes