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Author ORCID Identifier
N/A
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Mathematics
Year Degree Awarded
2015
Month Degree Awarded
September
First Advisor
Anna Liu
Subject Categories
Applied Statistics | Multivariate Analysis | Statistical Methodology | Statistical Models
Abstract
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.
DOI
https://doi.org/10.7275/7289308.0
Recommended Citation
Wang, Peng, "Variable selection in single index varying coefficient models with LASSO" (2015). Doctoral Dissertations. 441.
https://doi.org/10.7275/7289308.0
https://scholarworks.umass.edu/dissertations_2/441
Included in
Applied Statistics Commons, Multivariate Analysis Commons, Statistical Methodology Commons, Statistical Models Commons