Wang Shao Hsuan
National Taiwan University
Taiwan
Title: Generalized concordance measure: Generalized regression model and dimension reduction
Biography
Biography: Wang Shao Hsuan
Abstract
In the scientific research literature, rank-based measures have been widely used to characterize a monotonic association between a univariate response and some transformation of multiple covariates of interest. Instead of using a linear combination of covariates, we introduce a multivariate polynomial score to compute the corresponding concordance index through more general semi-parametric regression models. It involves the estimation for the degree of the multivariate polynomial and the central subspace (CS). To deal with this research issue, we propose a BIC-type estimation approach, which is implemented by an effective computational algorithm, to achieve the model selection consistency.
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