Group Selection in Semiparametric and Nonparametric Accelerated Failure Time Models

Simple item page
atmire.migration.oldid5237
dc.contributor.advisorLu, Xuewen
dc.contributor.advisorKopciuk, Karen
dc.contributor.authorHuang, Longlong
dc.contributor.committeememberDeardon, Rob
dc.contributor.committeememberSajobi, Tolulope
dc.contributor.committeememberYan, Ying
dc.contributor.committeememberHu, Joan
dc.date.accessioned2017-01-06T17:57:17Z
dc.date.available2017-01-06T17:57:17Z
dc.date.issued2017
dc.date.submitted2017en
dc.description.abstractIn survival analysis, a number of regression models can be used to estimate the effects of covariates on the censored survival outcome. When covariates can be naturally grouped, group selection is important in these models. Motivated by the group bridge approach for variable selection in a multiple linear regression model, we consider group selection in a semiparametric accelerated failure time (AFT) model using Stute's weighted least squares and a group bridge penalty. This method is able to simultaneously carry out feature selection at both the group and within-group individual variable levels and enjoys the powerful oracle group selection property. Although the group bridge penalized approach can effectively remove unimportant groups, it cannot effectively remove unimportant variables within the important groups. To overcome this limitation, the adaptive group bridge method is proposed. We show that the adaptive group bridge method obtains the oracle property. Simulation studies indicate that the group bridge and adaptive group bridge approaches for the AFT model can correctly identify important groups and variables even with high censoring rates. A real data analysis is provided to illustrate the application of the proposed methods. We further study a nonparametric accelerated failure time additive regression (NP-AFT-AR) model whose covariates have nonparametric effects on the survival time. The proposed model is more flexible than the linear model and can be fitted to high-dimensional censored data when some components are unknown non-linear functions. B-splines are used to approximate the nonparametric components. A group bridge penalized variable selection approach based on the inverse probability-of-censoring weighted least squares is developed to select nonparametric components. The proposed approach is able to distinguish the nonzero components from the zero components and estimate the nonzero components simultaneously. Computational algorithms and theoretical properties of the proposed method are established. Simulation studies indicate that the proposed method has satisfactory performance even with relatively high censoring rates. Two real data analyses are used to illustrate the application of the proposed method to survival data analysis.en_US
dc.identifier.citationHuang, L. (2017). Group Selection in Semiparametric and Nonparametric Accelerated Failure Time Models (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26330en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/26330
dc.identifier.urihttp://hdl.handle.net/11023/3546
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectStatistics
dc.titleGroup Selection in Semiparametric and Nonparametric Accelerated Failure Time Models
dc.typedoctoral thesis
thesis.degree.disciplineMathematics and Statistics
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
Files
Collections
Open Theses and Dissertations