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Doctor of Philosophy (PhD)
Year Degree Awarded
Month Degree Awarded
Applied Statistics | Multivariate Analysis | Statistical Methodology | Statistical Models
Single index varying coefficient model is a very attractive statistical model due to its ability to reduce dimensions and easy-of-interpretation. There are many theoretical studies and practical applications with it, but typically without features of variable selection, and no public software is available for solving it. Here we propose a new algorithm to fit the single index varying coefficient model, and to carry variable selection in the index part with LASSO. The core idea is a two-step scheme which alternates between estimating coefficient functions and selecting-and-estimating the single index. Both in simulation and in application to a Geoscience dataset, we showed that it works very well. We also presented our R package "sivcm" with the algorithm implemented and with ideas that can be extended beyond.
Wang, Peng, "Variable selection in single index varying coefficient models with LASSO" (2015). Doctoral Dissertations. 441.