The crisis periods of the past decades have highlighted the difficulty of forecasting economic indicators due to increased non-linearity and rapidly changing dynamics. To address this challenge, we introduce the Transform-Sparsify-Forecast (TSF) framework. The TSF framework first applies...

The crisis periods of the past decades have highlighted the difficulty of forecasting economic indicators due to increased non-linearity and rapidly changing dynamics. To address this challenge, we introduce the Transform-Sparsify-Forecast (TSF) framework. The TSF framework first applies...

The paper is devoted to discussing consequences of the so-called Frisch-Waugh Theorem to posterior inference and Bayesian model comparison. We adopt a generalised normal linear regression framework and weaken its assumptions in order to cover non-normal, jointly elliptical sampling...

In the research area of crop yield density estimation and in particular in risk analysis, little emphasis has been given to the appropriateness of transformation methods (e.g., removing a linear trend) and how such transformations impact the reliability of the empirical distribution functions...

In the research area of crop yield density estimation and in particular in risk analysis, little emphasis has been given to the appropriateness of transformation methods (e.g., removing a linear trend) and how such transformations impact the reliability of the empirical distribution functions...

This paper introduces some robust estimation procedures to estimate quantiles of a continuous random variable based on data, without any other assumptions of probability distribution. We construct a reasonable linear regression model to connect the relationship between a suitable symmetric data...