Sliced inverse regression for survival data

• Published in: Statistical papers (Berlin, Germany) Vol. 55; no. 1; pp. 209 - 220
• Main Authors: Shevlyakova, Maya, Morgenthaler, Stephan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2014; Springer Nature B.V
• Subjects: Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Failure; Finance; Insurance; Inverse; Management; Mathematics and Statistics; Operations Research/Decision Theory; Principal components analysis; Probability Theory and Stochastic Processes; Random variables; Regression analysis; Regular Article; Statistics; Statistics for Business; Studies; Survival analysis; Sliced inverse regression; Survival data; Variable selection
• ISSN: 0932-5026; 1613-9798; 1613-9798
• DOI: 10.1007/s00362-013-0552-8

We apply the univariate sliced inverse regression to survival data. Our approach is different from the other papers on this subject. The right-censored observations are taken into account during the slicing of the survival times by assigning each of them with equal weight to all of the slices with longer survival. We test this method with different distributions for the two main survival data models, the accelerated lifetime model and Cox’s proportional hazards model. In both cases and under different conditions of sparsity, sample size and dimension of parameters, this non-parametric approach finds the data structure and can be viewed as a variable selector.