Abstract
This paper revisits two bivariate Pareto models for fitting competing risks data. The first model is the Frank copula model, and the second one is a bivariate Pareto model introduced by Sankaran and Nair (1993). We discuss the identifiability issues of these models and develop the maximum likelihood estimation procedures including their computational algorithms and model-diagnostic procedures. Simulations are conducted to examine the performance of the maximum likelihood estimation. Real data are analyzed for illustration.
Original language | English |
---|---|
Pages (from-to) | 1193-1220 |
Number of pages | 28 |
Journal | Communications in Statistics - Theory and Methods |
Volume | 48 |
Issue number | 5 |
DOIs | |
State | Published - 4 Mar 2019 |
Keywords
- Bivariate Pareto distribution
- Frank copula
- Kendall's tau
- Newton-Raphson algorithm
- Survival analysis
Fingerprint
Dive into the research topics of 'Fitting competing risks data to bivariate Pareto models'. Together they form a unique fingerprint.Datasets
-
Fitting competing risks data to bivariate Pareto models
Lee, W. (Contributor), Sun, L.-H. (Contributor), Emura, T. (Contributor) & Shih, J.-H. (Contributor), figshare Academic Research System, 4 Mar 2019
DOI: 10.6084/m9.figshare.5895925.v1, https://doi.org/10.6084%2Fm9.figshare.5895925.v1
Dataset