| Full text | |
| Author(s): |
Hashimoto, Elizabeth M.
[1]
;
Silva, Giovana O.
[2]
;
Ortega, Edwin M. M.
[3]
;
Cordeiro, Gauss M.
[4]
Total Authors: 4
|
| Affiliation: | [1] UTFPR, Dept Acad Matemat, Curitiba, Parana - Brazil
[2] Univ Fed Bahia, Dept Estat, Salvador, BA - Brazil
[3] Univ Sao Paulo, Dept Ciencias Exatas, ESALQ, Av Padua Dias 11, Caixa Postal 9, BR-13418900 Piracicaba, SP - Brazil
[4] Univ Fed Pernambuco, Dept Estat, Recife, PE - Brazil
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | JOURNAL OF STATISTICAL THEORY AND PRACTICE; v. 13, n. 2 JUN 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
It may happen in some applications that the assumption of independence of survival times does not hold. Thus, we propose a new log-Burr XII regression model with log-gamma-Weibull distributions for the random effects. The maximum likelihood method is used to estimate the model parameters based on the Gauss-Hermite numerical integration technique. For different parameter settings, sample sizes, censoring percentages and correlated data, various simulations are performed. Some global-influence measurements are also investigated. In order to assess the robustness of the maximum likelihood estimators, we evaluate local influence on the estimates of the parameters under different perturbation schemes. We illustrate the importance of the new model by means of a real data set in analysis of experiments. (AU) | |
| FAPESP's process: | 10/04496-2 - Gamma-G regression model in survival analysis |
| Grantee: | Elizabeth Mie Hashimoto |
| Support Opportunities: | Scholarships in Brazil - Doctorate |