A comparison of parametric and semi-parametric survival models with artificial neural networks. Issue 3 (16th March 2018)

• Record Type: Journal Article
• Authors: Mokarram, Reza; Emadi, Mahdi; Rad, Arezou Habibi; Nooghabi, Mahdi Jabbari
• Is Part Of: Communications in statistics. Volume 47:Issue 3(2018)
• Page Start: 738
• Page End: 746
• Publication Date: 2018-03-16
• Subjects: Artificial Neural Networks -- Cox model -- Parametric model -- Proportional Hazard; 62N01; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2017.1291961
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Survival models are used to examine data in the event of an occurrence. These are discussed in various types including parametric, non-parametric and semi-parametric models. Parametric models require a clear distribution of survival time, and semi-parametric models assume proportional hazards. Among these models, the non-parametric model of artificial neural network has the fewest assumptions and can be often replaced by other models. Given the importance of distribution Weibull survival models in this study of simulation shape parameter of the Weibull distribution have been assumed as 1, 2 and 3, and also the average rate at levels of 0%–75% have been censored. The values predicted by the neural network forecasting model with parametric survival and Cox regression models were compared. This comparison considering levels of complexity due to the hazard model using the ROC curve and the corresponding tests have been carried out.