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Statistical Analysis of Response Distribution for Dependent Data via Joint Quantile Regression

dc.contributor.advisor Tokdar, Surya T
dc.contributor.author Chen, Xu
dc.date.accessioned 2021-09-14T15:09:25Z
dc.date.available 2021-09-14T15:09:25Z
dc.date.issued 2021
dc.identifier.uri https://hdl.handle.net/10161/23821
dc.description Dissertation
dc.description.abstract <p>Linear quantile regression is a powerful tool to investigate how predictors may affect a response heterogeneously across different quantile levels. Unfortunately, existing approaches find it extremely difficult to adjust for any dependency between observation units, largely because such methods are not based upon a fully generative model of the data. In this dissertation, we address this difficulty for analyzing spatial point-referenced data and hierarchical data. Several models are introduced by generalizing the joint quantile regression model of Yang and Tokdar (2017) and characterizing different dependency structures via a copula model on the underlying quantile levels of the observation units. A Bayesian semiparametric approach is introduced to perform inference of model parameters and carry out prediction. Multiple copula families are discussed for modeling response data with tail dependence and/or tail asymmetry. An effective model comparison criterion is provided for selecting between models with different combinations of sets of predictors, marginal base distributions and copula models.</p><p>Extensive simulation studies and real applications are presented to illustrate substantial gains of the proposed models in inference quality, prediction accuracy and uncertainty quantification over existing alternatives. Through case studies, we highlight that the proposed models admit great interpretability and are competent in offering insightful new discoveries of response-predictor relationship at non-central parts of the response distribution. The effectiveness of the proposed model comparison criteria is verified with both empirical and theoretical evidence.</p>
dc.subject Statistics
dc.subject Copula
dc.subject Gaussian process
dc.subject Hierarchical data
dc.subject Longitudinal data
dc.subject Quantile regression
dc.subject Spatial data
dc.title Statistical Analysis of Response Distribution for Dependent Data via Joint Quantile Regression
dc.type Dissertation
dc.department Statistical Science


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