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A0467
Title: Estimation of the sufficient dimension reduction score with censored survival data Authors:  Shao-Hsuan Wang - National Central University (Taiwan) [presenting]
Abstract: Sufficient dimension reduction (SDR) is widely used for the analysis of high-dimensional data of complex structure. The concept of sufficient dimension reduction (SDR) score is first introduced, highlighting the importance and challenges for statistical analysis. To characterize the dependence of a response on covariates of interest, a monotonic structure is linked to a multivariate polynomial transformation of the central subspace (CS) directions with unknown structural degree and dimension. Under a very general semiparametric model formulation, such a sufficient dimension reduction (SDR) score is shown to enjoy the existence, optimality, and uniqueness up to scale and location in the defined concordance probability function. Two types of concordance-based generalized Bayesian information criteria are given to estimate the optimal SDR score and the maximum concordance index, denoted by Cmax. The estimation criteria are further carried out by effective computational procedures. The performance and practicality of the proposals are also investigated through simulations and empirical illustrations.