Model selection among Dimension-Reduced generalized Cox models

Ming-Yueh Huang () and Kwun Chuen Gary Chan
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Ming-Yueh Huang: Institute of Statistical Science, Academia Sinica
Kwun Chuen Gary Chan: University of Washington

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 3, No 6, 492-511

Abstract: Abstract Conventional semiparametric hazards regression models rely on the specification of particular model formulations, such as proportional-hazards feature and single-index structures. Instead of checking these modeling assumptions one-by-one, we proposed a class of dimension-reduced generalized Cox models, and then a consistent model selection procedure among this class to select covariates with proportional-hazards feature and a proper model formulation for non-proportional-hazards covariates. In this class, the non-proportional-hazards covariates are treated in a nonparametric manner, and a partial sufficient dimension reduction is introduced to reduce the curse of dimensionality. A semiparametric efficient estimation is proposed to estimate these models. Based on the proposed estimation, we further constructed a cross-validation type criterion to consistently select the correct model among this class. Most importantly, this class of hazards regression models contains the fully nonparametric hazards regression model as the most saturated submodel, and hence no further model diagnosis is required. Overall speaking, this model selection approach is more effective than performing a sequence of conventional model checking. The proposed method is illustrated by simulation studies and a data example.

Keywords: Information bound; Partial sufficient dimension reduction; Proportional-hazards; Right-censoring; 62G018; 62N02 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-022-09565-5

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