Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/69082
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTang, Xingyuen
dc.date.accessioned2016-10-20T09:23:28Zen
dc.date.available2016-10-20T09:23:28Zen
dc.date.issued2016en
dc.identifier.citationTang, X. (2016). Boosting for partially linear additive models. Doctoral thesis, Nanyang Technological University, Singapore.en
dc.identifier.urihttps://hdl.handle.net/10356/69082en
dc.description.abstractAdditive 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 with the partially linear additive model is to identify nonlinear, linear, and non-informative covariates with no such pre-specification given, and to simultaneously recover underlying component functions which indicate how each covariate affects the response. In this thesis, algorithms are developed to solve the above question. Main technique used is gradient boosting, in which simple linear regressions and univariate penalized splines are together used as base learners. In this way our proposed algorithms are able to estimate component functions and simultaneously specify model structure. Twin boosting is incorporated as well to achieve better variable selection accuracy. The proposed methods can be applied to mean and quantile regressions as well as survival analysis. Simulation studies as well as real data applications illustrate the strength of our proposed approaches.en
dc.format.extent221 p.en
dc.language.isoenen
dc.subjectDRNTU::Science::Chemistry::Analytical chemistry::Quantitative analysisen
dc.titleBoosting for partially linear additive modelsen
dc.typeThesisen
dc.contributor.supervisorQin Yinglien
dc.contributor.supervisorLian Hengen
dc.contributor.supervisorXiang Limingen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen
dc.description.degreeDOCTOR OF PHILOSOPHY (SPMS)en
dc.identifier.doi10.32657/10356/69082en
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:SPMS Theses
Files in This Item:
File Description SizeFormat 
Thesis.pdf17.78 MBAdobe PDFThumbnail
View/Open

Page view(s) 50

586
Updated on Feb 10, 2025

Download(s) 20

309
Updated on Feb 10, 2025

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.