Nonparametric predictive inference with parametric copula for survival analysis
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 Pr...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
EDP Sciences
2018
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/22881/ http://umpir.ump.edu.my/id/eprint/22881/ http://umpir.ump.edu.my/id/eprint/22881/1/matecconf_meamt2018_03026.pdf |
| Summary: | 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 |
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