Nonparametric predictive inference with parametric copula for survival analysis

Noryanti, Muhammad and Yusoff, N. (2018) Nonparametric predictive inference with parametric copula for survival analysis. In: MATEC Web of Conferences: 2nd International Conference on Material Engineering and Advanced Manufacturing Technology, MEAMT 2018, 25-27 May 2018 , Beijing; China. pp. 1-6., 189.


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Many real-world problems of statistical inference involve dependent bivariate data including survival analysis. This paper presents new nonparametric methods for predictive inference for survival analysis involving a future bivariate observation. The method combine between bivariate Nonparametric Predictive Inference (NPI) for the marginals with parametric copula to take dependence structure into account. The proposed method is a discretized version of the parametric copula. The NPI fits the marginal and very straight forward computations. Generally, NPI is a frequentist approach which infer a future observation based on past data. The proposed method resulting imprecision is robustness with regard to the assumed parametric copula in the marginal for prediction. This is practical for small data set. The suggestion is to use a basic parametric copula for small data sets. We investigate and discuss the performance of these methods by presenting results from simulation studies. The method is further illustrated via application in survival analysis using data sets from the literature

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Index by Scopus
Uncontrolled Keywords: Dependence structures; Forward computation; Future observations; Nonparametric methods
Subjects: Q Science > QA Mathematics
Faculty/Division: Faculty of Industrial Sciences And Technology
Depositing User: Dr Noryanti Muhammad
Date Deposited: 24 Dec 2018 01:53
Last Modified: 16 Apr 2019 04:27
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