I. Gómez-Méndez and E. Joly, "Regression with missing data, a comparison study of techniques based on random forests," Journal of Statistical Computation and Simulation, vol. 93, no. 12, pp. 1924–1949, 2023. doi: 10.1080/00949655.2022.2163646.
I. Gómez-Méndez and C. Amornbunchornvej, "Income, education, and other poverty-related variables: A journey through Bayesian hierarchical models," Heliyon, vol. 10, no. 6, art. no. e27968, Mar. 2024. doi: 10.1016/j.heliyon.2024.e27968.
I. Gómez-Méndez and E. Joly, "On the consistency of a random forest algorithm in the presence of missing entries," Journal of Nonparametric Statistics, vol. 36, no. 2, pp. 400–434, 2024. doi: 10.1080/10485252.2023.2219783.
I. Gómez-Méndez and C. Amornbunchornvej, "Regional and spatial dependence of poverty factors in Thailand, and its use in Bayesian hierarchical regression analysis," Statistical Journal of the IAOS, vol. 42, no. 1, pp. 322–337, 2026.
I. Gómez-Méndez, "Random Forests and Autoencoders with Missing Data," Ph.D. dissertation, Centro de Investigación en Matemáticas (CIMAT), Guanajuato, Mexico, 2021.
[1] I. Gómez-Méndez and E. Joly, "Regression with missing data, a comparison study of techniques based on random forests," Journal of Statistical Computation and Simulation, vol. 93, no. 12, pp. 1924–1949, 2023. doi: 10.1080/00949655.2022.2163646.
[2] I. Gómez-Méndez and C. Amornbunchornvej, "Income, education, and other poverty-related variables: A journey through Bayesian hierarchical models," Heliyon, vol. 10, no. 6, art. no. e27968, Mar. 2024. doi: 10.1016/j.heliyon.2024.e27968.
[3] I. Gómez-Méndez and E. Joly, "On the consistency of a random forest algorithm in the presence of missing entries," Journal of Nonparametric Statistics, vol. 36, no. 2, pp. 400–434, 2024. doi: 10.1080/10485252.2023.2219783.
[4] I. Gómez-Méndez and C. Amornbunchornvej, "Regional and spatial dependence of poverty factors in Thailand, and its use into Bayesian hierarchical regression analysis," arXiv preprint arXiv:2408.09760, 2024.
[5] I. Gómez-Méndez, "Random Forests and Autoencoders with Missing Data," Ph.D. dissertation, Centro de Investigación en Matemáticas (CIMAT), Guanajuato, Mexico, 2021.
Computing Skills
Programming Languages: Python, Julia, R
Data Visualization: Plotly, Tableau, Shiny
Deep Learning: TensorFlow
Big Data: PySpark
Bayesian Analysis: PyMC
Other Data Analysis: SPSS, Google Analytics
Other Languages: SQL, HTML, CSS, LATEX
2021: Doctor of Philosophy in Probability and Statistics, Centro de Investigación en Matemáticas (Center for Research in Mathematics, CIMAT), Mexico
2016: Master of Science in Probability and Statistics, Centro de Investigación en Matemáticas (Center for Research in Mathematics, CIMAT), Mexico
2014: Bachelor of Engineering in Mathematics, Instituto Politécnico Nacional (National Polytechnique Institute, IPN), Mexico
