Jorge Loría

Jorge Loría

PhD in Statistics, interested in inference using heavy tails and Bayesian methods

Publications

Learning to Rank by Causal Effects Without Data to Accurately Estimate Causal Effects

Decision makers often care about the causal effect to select the top customers in a specific treatment, we propose a framework on how to do this even when the data does not lend itself to accurately estimate causal effects.

Recommended citation: Fernández-Loría, C, Loría J. (2023+). " Learning to Rank by Causal Effects Without Data to Accurately Estimate Causal Effects ." submitted. https://arxiv.org/pdf/2206.12532.pdf

SURE-tuned Bridge Regression

Bridge is a regularization technique that can consume a lot of time when the number of covariates is much larger than the number of observations. We propose a non-iterative method to reduce this timing without losing statistical prediction power.

Recommended citation: Loria, J, Bhadra, A. (2024). "SURE-tuned Bridge Regression." Statistics and Computing 34, 30. https://link.springer.com/article/10.1007/s11222-023-10350-z

Demographic Modeling via 3-dimensional Markov Chains

We propose a new model for understanding and prediction of demographic modeling, using Markov Chains. We apply it to an institution in Costa Rica.

Recommended citation: Víquez, J. J., Víquez, J. A., Campos, A., Loría, J., & Mendoza, L. A. (2018). Modelación de poblaciones vía cadenas de Markov tridimensionales. Revista De Matemática: Teoría Y Aplicaciones, 25(2), 185–214. https://doi.org/10.15517/rmta.v25i2.33608. https://revistas.ucr.ac.cr/index.php/matematica/article/view/33608/34172