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Full-Text Articles in Statistical Models
Dimension Reduction Techniques In Regression, Pei Wang
Dimension Reduction Techniques In Regression, Pei Wang
Theses and Dissertations--Statistics
Because of the advances of modern technology, the size of the collected data nowadays is larger and the structure is more complex. To deal with such kinds of data, sufficient dimension reduction (SDR) and reduced rank (RR) regression are two powerful tools. This dissertation focuses on these two tools and it is composed of three projects. In the first project, we introduce a new SDR method through a novel approach of feature filter to recover the central mean subspace exhaustively along with a method to determine the dimension, two variable selection methods, and extensions to multivariate response and large p …
The Family Of Conditional Penalized Methods With Their Application In Sufficient Variable Selection, Jin Xie
The Family Of Conditional Penalized Methods With Their Application In Sufficient Variable Selection, Jin Xie
Theses and Dissertations--Statistics
When scientists know in advance that some features (variables) are important in modeling a data, then these important features should be kept in the model. How can we utilize this prior information to effectively find other important features? This dissertation is to provide a solution, using such prior information. We propose the Conditional Adaptive Lasso (CAL) estimates to exploit this knowledge. By choosing a meaningful conditioning set, namely the prior information, CAL shows better performance in both variable selection and model estimation. We also propose Sufficient Conditional Adaptive Lasso Variable Screening (SCAL-VS) and Conditioning Set Sufficient Conditional Adaptive Lasso Variable …