Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.
Author ORCID Identifier
Campus-Only Access for Five (5) Years
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.