Deep learning for the partially linear Cox model
Author:
Affiliation:
1. Department of Statistics and Data Science, School of Economics, Xiamen University
2. Amazon Web Services
3. Department of Statistics, University of California, Davis
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference90 articles.
1. Chen, K., Guo, S., Sun, L. and Wang, J.-L. (2010). Global partial likelihood for nonparametric proportional hazards models. J. Amer. Statist. Assoc. 105 750–760.
2. Cox, D. R. (1975). Partial likelihood. Biometrika 62 269–276.
3. Hornik, K., Stinchcombe, M. and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Netw. 2 359–366.
4. Horowitz, J. L. (2009). Semiparametric and Nonparametric Methods in Econometrics. Springer Series in Statistics. Springer, New York.
5. Barron, A. R. (1994). Approximation and estimation bounds for artificial neural networks. Mach. Learn. 14 115–133.
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