This paper explores the properties of using a generalized additive model with embedded variable selection for the prediction of bankruptcy. The main purpose is to explore an innovative way to close the gap between interpretation and prediction that has prevented widespread use of methods based on machine learning. An additive model enables the incorporation of nonlinear effects for each predictor, thereby enhancing the predictive power over classical linear models, while simultaneously keeping the marginal effects for interpretation separated. In addition, we propose a penalization likelihood approach that automatically selects important financial ratios and classifies them under linear and nonlinear effects, thereby improving the interpretation of the estimations.
https://www.tandfonline.com/doi/pdf/10.1080/23322039.2019.1597956
Sergio Cabrales, experto en temas energéticos, aseguró que Colombia cuenta con gas para abastecer la…
“Solamente dos pequeños parques eólicos han sido construidos: Guajira 1 y WESP, y se encuentran…
https://twitter.com/SergioCabrales/status/1891481230903771417
https://youtu.be/aP_gBHdMMKo
El nuevo acuerdo suscrito por la petrolera con OXY busca el desarrollo de 91 pozos…
Las compras al exterior del combustible han aumentado significativamente; mientras Ecopetrol anunció que ampliará su…