A Semiparametric spatial model is used as it allows nonlinear estimation of both mean and variance.<br /><br /> A Bayesian approach is used for inference via a Markov Chain Monte Carlo sampling scheme. A distinct advantage of using the Bayesian approach is the incorporation of prior information in the...

A nonparametric and locally adaptive Bayesian estimator is proposed for estimating a binary regression. Flexibility is obtained by modeling the binary regression as a mixture of probit regressions with the argument of each probit regression having a thin plate spline prior with its own smoothing...

This paper identifies the potential to improve ESG credentials of a given reference portfolio whilst broadly maintaining risk-adjusted return characteristics, hence anchoring the portfolio to a better ESG profile. ‘Improving’ in this case means allocating a higher weight to better ESG stocks...

We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model, and whether they enter linearly or flexibly. When the variance term is null we obtain a...