Abstract
Varying-coefficient models (VCMs) are useful tools for analysing longitudinal data. They can effectively describe the relationship between predictors and responses repeatedly measured. VCMs estimated by regularization methods are strongly affected by values of regularization parameters, and therefore selecting these values is a crucial issue. In order to choose these parameters objectively, we derive model selection criteria for evaluating VCMs from the viewpoints of information-theoretic and Bayesian approach. Models are estimated by the method of regularization with basis expansions, and then they are evaluated by model selection criteria. We demonstrate the effectiveness of the proposed criteria through Monte Carlo simulations and real data analysis.
Original language | English |
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Pages (from-to) | 2156-2165 |
Number of pages | 10 |
Journal | Journal of Statistical Computation and Simulation |
Volume | 84 |
Issue number | 10 |
DOIs | |
Publication status | Published - Oct 2014 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation
- Statistics, Probability and Uncertainty
- Applied Mathematics