Based on a record sample from the Rayleigh model, we consider the problem of estimating the scale and location parameters of the model and predicting the future unobserved record data. Maximum likelihood and Bayesian approaches under different loss functions are used to estimate the model's...

Bayesian inference relies heavily on numerical Markov chain Monte carlo (MCMC) methods for the estimation of the typically intractable high-dimensional posterior distributions and requires specific inputs. In this paper we introduce a new general and efficient numerical approach to address...

In recent years, environmental protection has been a priority goal in tourism policies worldwide, including the regional comprehensive economic partnership (RCEP). This study is conducted to explore the impact of tourism, economic growth, globalization, and human capital on environmental...

We propose a Bayesian Metropolis-Gibbs Monte Carlo Markov Chain (MCMC) algorithm to estimate parameters of a sample selection model in which the selected equation include a binary endogenous explanatory variable, using a three simultaneous equation model. We apply our methodology to...