A quadratic upper bound algorithm for regression analysis of credit risk under the proportional hazards model with case-cohort data

A case-cohort design is a cost-effective biased-sampling scheme in studies on survival data. We study the regression analysis of credit risk by fitting the proportional hazards model to data collected via the case-cohort design. Using the minorization-maximization principle, we develop a new quadratic upper-bound algorithm for the calculation of estimators and obtain the convergence of the algorithm. The proposed algorithm involves the inversion of the derived upper-bound matrix only one time in the whole process and the upper-bound matrix is independent of parameters. These features make the proposed algorithm have simple update and low per-iterative cost, especially to large-dimensional problems. Rcpp is an R package which enables users to write R extensions with C++. In this paper, we write the program of the proposed algorithm via Rcpp and improve the efficiency of R program execution and realize the fast computing. We conduct simulation studies to illustrate the performance of the proposed algorithm. We analyze a real data example from a mortgage dataset for evaluating credit risk.