Boosting for partially linear additive models

• Main Author: Tang, Xingyu
• Other Authors: Qin Yingli
• Format: Theses and Dissertations
• Language: English
• Published: 2016
• Subjects: DRNTU::Science::Chemistry::Analytical chemistry::Quantitative analysis
• Online Access: https://hdl.handle.net/10356/69082
• Institution: Nanyang Technological University

Additive models are widely applied in statistical learning. The partially linear additive model is a special form of additive models, which combines the strengths of linear and nonlinear models by allowing linear and nonlinear predictors to coexist. One of the most interesting questions associated w...