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Published in:
2017 | OriginalPaper | Chapter
7. Variable Selection for Frailty Models
Authors : Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
Published in: Statistical Modelling of Survival Data with Random Effects
Publisher: Springer Singapore
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Abstract
Including only the relevant variables in the model is crucial in statistical inference, improving the quality of estimation, prediction, and interpretation. If there exist many potential variables with equal status, i.e., no prior preference among them, then having as few variables as possible in the model would often facilitate clearer interpretation. When there are many potential variables, over-fitting can become a serious problem. However, missing relevant variables would be also undesirable.