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Full-Text Articles in Statistical Models
Variable Selection In Single Index Varying Coefficient Models With Lasso, Peng Wang
Variable Selection In Single Index Varying Coefficient Models With Lasso, Peng Wang
Doctoral Dissertations
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 …
Seasonal Decomposition For Geographical Time Series Using Nonparametric Regression, Hyukjun Gweon
Seasonal Decomposition For Geographical Time Series Using Nonparametric Regression, Hyukjun Gweon
Electronic Thesis and Dissertation Repository
A time series often contains various systematic effects such as trends and seasonality. These different components can be determined and separated by decomposition methods. In this thesis, we discuss time series decomposition process using nonparametric regression. A method based on both loess and harmonic regression is suggested and an optimal model selection method is discussed. We then compare the process with seasonal-trend decomposition by loess STL (Cleveland, 1979). While STL works well when that proper parameters are used, the method we introduce is also competitive: it makes parameter choice more automatic and less complex. The decomposition process often requires that …