Bayesian synthetic likelihood (BSL) is an established method for performing approximate inference when the function intractable. In methods, approximated parametrically via model simulations, and then standard likelihood-based techniques are used to perform inference. The Gaussian estimator has become ubiquitous in BSL literature, primarily its simplicity ease of implementation. However, it oft...

Introduction: There is a lack of information on the extent of dependency between chronic diseases and the survival rate of breast cancer. Until date, none of the models proposed has determined the impact of chronic diseases on breast cancer survival. This study, therefore, aimed to investigate the impacts of chronic diseases such as diabetes, blood pressure, and endocrine di...

Introduction: There is a lack of information on the extent of dependency between chronic diseases and the survival rate of breast cancer. Until date, none of the models proposed has determined the impact of chronic diseases on breast cancer survival. This study, therefore, aimed to investigate the impacts of chronic diseases such as diabetes, blood pressure, and endocrine di...

The purpose of this study is estimation of daily Value at Risk (VaR) for total index of Tehran Stock Exchange using parametric, nonparametric and semi-parametric approaches. Conditional and unconditional coverage backtesting are used for evaluating the accuracy of calculated VaR and also to compare the performance of mentioned approaches. In most cases, based on backtesting statistics Results, ...

Present study is about using of nonparametric models for GDP (Gross Domestic Product) per capita prediction in Turkey. It has been considered two alternative situations due to seasonal effects. In the first case, it is discussed a semi-parametric model where parametric component is dummy variable for the seasonality. In the second case, it is considered the seasonal component to be a smooth fun...