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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

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teaching

Teaching at Purdue University

  • STAT 350: Introduction to Statistics (Fall 2019a,Fall 2022t)
  • STAT 301: Elements of Statistics (Fall 2019a, Spring 2020a, Fall 2020a, Spring 2021a, Fall 2021t, Spring 2022t, Summer 2022t)