Robust regularized singular value decomposition with application to mortality data
KAUST Grant NumberKUS-C1-016-04
Permanent link to this recordhttp://hdl.handle.net/10754/599527
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AbstractWe develop a robust regularized singular value decomposition (RobRSVD) method for analyzing two-way functional data. The research is motivated by the application of modeling human mortality as a smooth two-way function of age group and year. The RobRSVD is formulated as a penalized loss minimization problem where a robust loss function is used to measure the reconstruction error of a low-rank matrix approximation of the data, and an appropriately defined two-way roughness penalty function is used to ensure smoothness along each of the two functional domains. By viewing the minimization problem as two conditional regularized robust regressions, we develop a fast iterative reweighted least squares algorithm to implement the method. Our implementation naturally incorporates missing values. Furthermore, our formulation allows rigorous derivation of leaveone- row/column-out cross-validation and generalized cross-validation criteria, which enable computationally efficient data-driven penalty parameter selection. The advantages of the new robust method over nonrobust ones are shown via extensive simulation studies and the mortality rate application. © Institute of Mathematical Statistics, 2013.
CitationZhang L, Shen H, Huang JZ (2013) Robust regularized singular value decomposition with application to mortality data. The Annals of Applied Statistics 7: 1540–1561. Available: http://dx.doi.org/10.1214/13-AOAS649.
SponsorsSupported in part by NIH/NIDA (1 RC1 DA029425-01) and NSF (CMMI-0800575, DMS-11-06912).Supported in part by NCI (CA57030), NSF (DMS-09-07170, DMS-10-07618, DMS-12-08952) and Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
PublisherInstitute of Mathematical Statistics
JournalThe Annals of Applied Statistics