Description
Chair Corrado Crocetta
Data continues to become more abundant, and so the datasets that contain it. Even though big datasets can present insights and opportunities, they can pose significant challenges when it comes to statistical analysis. One of the biggest challenges, required to process and analyze large datasets, is the computational resources. Regression can be problematic in case of big datasets, due to the...
In this work, focus is given in the Bayesian variable selection problem for high-dimensional linear regression problems. The use of shrinkage priors, when the number n of available observations is less than the number p of explanatory variables, is a well-established method, which shares great theoretical and empirical properties. By using imaginary data and shrinkage priors as baseline...
In many empirical domains, the availability of ultrahigh-dimensional data has led to the development of feature screening and variable selection procedures aiming to detect the informative variables of datasets and consequently remove unimportant features.
In this context, we propose a ranking-based variable selection procedure that extends the Ranking Based Variable Selection technique...
