Use of High Dimensional Modeling for automatic variables selection: the best path algorithm
This addresses variable selection for researchers and practitioners working with high-dimensional data, but it appears incremental as it builds on existing methods like LASSO.
The paper tackles the problem of automatic variable selection in large datasets by introducing a new algorithm that leverages graphical models and can be combined with various forecasting models; it demonstrates results by comparing with the LASSO method using OLS, but no concrete numbers are provided.
This paper presents a new algorithm for automatic variables selection. In particular, using the Graphical Models properties it is possible to develop a method that can be used in the contest of large dataset. The advantage of this algorithm is that can be combined with different forecasting models. In this research we have used the OLS method and we have compared the result with the LASSO method.