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Bayesian Reference Analysis for the Generalized Normal Linear Regression ModelTomazella, Vera Lucia Damasceno; orcid: 0000-0002-6780-2089; email: email@example.com; Jesus, Sandra Rêgo; email: firstname.lastname@example.org; Gazon, Amanda Buosi; orcid: 0000-0001-8140-5496; email: email@example.com; Louzada, Francisco; orcid: 0000-0001-7815-9554; email: firstname.lastname@example.org; Nadarajah, Saralees; email: email@example.com; Nascimento, Diego Carvalho; orcid: 0000-0002-3406-4518; email: firstname.lastname@example.org; Rodrigues, Francisco Aparecido; email: email@example.com; Ramos, Pedro Luiz; orcid: 0000-0002-5387-2457; email: firstname.lastname@example.org (MDPI, 2021-05-12)This article proposes the use of the Bayesian reference analysis to estimate the parameters of the generalized normal linear regression model. It is shown that the reference prior led to a proper posterior distribution, while the Jeffreys prior returned an improper one. The inferential purposes were obtained via Markov Chain Monte Carlo (MCMC). Furthermore, diagnostic techniques based on the Kullback–Leibler divergence were used. The proposed method was illustrated using artificial data and real data on the height and diameter of Eucalyptus clones from Brazil.
The impact of socio-demographic factors on the survival of cancer patients in ZimbabweOkorie, Idika E.; Moyo, Ricardo; Nadarajah, Saralees; email: email@example.com (Nature Publishing Group UK, 2021-06-10)Abstract: We provide a survival analysis of cancer patients in Zimbabwe. Our results show that young cancer patients have lower but not significant hazard rate compared to old cancer patients. Male cancer patients have lower but not significant hazard rate compared to female cancer patients. Race and marital status are significant risk factors for cancer patients in Zimbabwe.