2026-10-14T21:33:29Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34015040 2026-09-28T15:16:21Z category_7 category_16 category_21 category_734 category_931 category_811 category_69 category_135 portal_87 item_type_3 month_year_09_2026

Deep Transformation Model Tong Wang (87696) Shunqin Zhang (25132055) Sanguo Zhang (1390083) Jian Huang (7250) Shuangge Ma (31881) Medicine Physiology Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Inorganic Chemistry Computational Biology Approximation to indicator function Deep neural network Transformation model Rank estimation <p>The increasing data complexity leads to the demand for more general and flexible modeling approaches. We consider a nonparametric transformation model with weak assumptions, which encompasses many popular models as special cases. A rank-based deep neural network (DNN) estimation approach is developed, with a loss function significantly different from the existing ones. It can accommodate outliers in response, which is a robustness property not broadly shared. For computational feasibility, we propose a double rectified linear unit (DReLU)-based estimator. To accommodate a diverging number of input variables and/or noises, we propose variable selection based on group penalization. We further expand the scope to accommodate censored survival data. We establish a new deep ReLU network approximation result as well as the estimation and variable selection properties. Numerical studies, including simulations and data analyses, establish practical utility of the proposed methods.</p> 2026-09-28T15:16:21Z Dataset Dataset 10.6084/m9.figshare.34015040.v1 https://figshare.com/articles/dataset/Deep_Transformation_Model/34015040 CC BY 4.0