Abstract
Joint modeling of mixed responses has become a popular research area due to its applicability in many disciplines. The interest of this study is joint modeling of survival and count data. Survival data is continuous in nature with censoring information combined to it, while count is a discrete variable. Due to this fact, joint modeling of these two variables will be a challenging task, but it will provide interesting and improved results than modeling these two variables separately. In this study, the concept of joint modeling of survival and count data has been carried out using two approaches: Bayesian modeling and Neural Networks, in order to compare their performances. The results of an application to the poultry data revealed that the Neural Network has a better fit in general.
Highlights
Joint modeling of data with mixed responses has become a popular research area in recent times
Many proposed criteria have a component that quantifies the goodness of model fit, along with a component that penalizes model complexity; namely, the Akaike information criterion (AIC), the Bayesian information criteria (BIC), and the deviance information criterion (DIC)
The authors of this study have previously proposed a joint model based on Generalized Linear Mixed Modeling (GLMM) approach (Hapugoda et al 2017)
Summary
Joint modeling of data with mixed responses has become a popular research area in recent times. J C Hapugoda, M R Sooriyarachchi researchers have considered parametric estimation methods, which deal with many distributional assumptions (Rizopolous, 2012, Gardiner 2013, Elashoff et al 2016). Recent literature reveals the popularity of semi-parametric approaches, such as Bayesian estimation methods, which deals with lesser distributional assumptions (Dunson and Herring, 2005, Bello et al, 2009, Chen et al, 2016). Though, it is not common among conventional statisticians to use nonparametric approaches like neural networks for model fitting, those can be accommodated for such joint modeling scenarios (Raman and Sunilkumar 1995, Pradhan and Lee, 2010). The overview of the paper is as follows: the literature review is presented the methodologies of the two approaches used in this study were explained in ‘Methodology’ section, the application of the concept to the poultry industry was described with a discussion in section ‘Analysis of Poultry Data’ and the conclusion of the study were presented in the ‘Conclusion’ section
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