Ramsés H. Mena UNAM Universidad Nacional Autónoma de México    

Publications in refereed journals

  1. Riva-Palacio, A., Mena, R.H. and Walker, S.G. (2023). On the estimation of partially observed continuous--time Markov chains. Computational Statistics. 38, 1357–1389
  2. Martínez-Ramos, M., González-Espinosa, M., Ramírez-Marcial, N., Negrete-Yankelevich, S. and Mena, R.H. (2023). Mid- and long-term ecological changes after enrichment planting with native tree species in Mexican tropical mountain forests. Restoration Ecology. 31-4, e13847.
  3. Gil-Leyva, M.F. and Mena, R.H. (2023). Stick-breaking processes with exchangeable length variables Journal of the American Statistical Association. 118, 537-550
  4. Martínez, A.F., Chaudhuri, S., Díaz-Avalos, C., Juan, P., Mateu, J. and Mena, R.H. (2023). Clustering constrained on linear networks. Stochastic Environmental Research and Risk Assessment. 37, 1983–1995.
  5. Pereira, L., Gutiérrez, L., Taylor-Rodríguez, D. and Mena, R.H. (2023). Bayesian nonparametric hypothesis testing for longitudinal data analysis. Computational Statistics and Data Analysis. 179, 107629.
  6. Rodríguez, C.E. and Mena, R.H. (2022). COVID-19 Clinical footprint to infer about mortality. Journal of the Royal Statistical Society. Series A. 185, S547-S572
  7. Ayala, D., Jofré, L, Gutiérrez, L. and Mena, R.H., (2022). On a Dirichlet process mixture representation of phase-type distributions. Bayesian Analysis. 17(3), 765-790.
  8. De Blasi, P., Mena, R.H. and Prünster, I. (2022). Asymptotic behavior of the number of distinct values in a sample from the geometric stick-breaking process. Annals of the Institute of Statistical Mathematics. 74, 143-165.
  9. Quinlan, J., Díaz-Avalos, C. and Mena, R.H. (2021). Modeling wildfires via marked spatio-temporal Poisson processes. Environmental and Ecological Statistics. 28, 549-565.
  10. Palma, F. and Mena, R.H. (2021). Duality for a class of continuous-time reversible Markov models. Statistics: A Journal of Theoretical and Applied Statistics. 55, 231-242.
  11. Mena, R.H., Velasco-Hernández, J., Mantilla, N., Carranco-Sapiens, G.A., Benet, L., Boyer, D. and Pérez-Castillo, I. (2020). Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City. Physical Biology 17, 065001.
  12. De Blasi, P., Martinez, A.F., Mena, R.H. and Prünster, I. (2020). On the inferential implications of decreasing weight structures in mixture models. Computational Statistics and Data Analysis. 147, 106940.
  13. Gutiérrez, L. Mena, R.H. and Díaz-Avalos, C. (2020). Linear models for statistical shape analysis based on parametrized closed curves. Statistical Papers. 61, 1213-1229.
  14. Gil-Leyva, M.F., Mena, R.H. and Nicoleris, T. (2020). Beta-Binomial stick-breaking non-parametric prior Electronic Journal of Statistics. 14, 1479-1507.
  15. Mena, R.H. and Palma, F. (2020). Continuous-time Markov processes, orthogonal polynomials and Lancaster probabilities ESAIM: Probability and Statistics (ESAIM: P&S). 24, 100-112.
  16. Coen, A., Gutiérrez, L. and Mena, R. (2019). Modeling failures times with dependent renewal type models via exchangeability. Statistics: A Journal of Theoretical and Applied Statistics. 53, 1112-1130.
  17. Damien, P., Fuentes-García, R., Mena, R. and Zarnikau, J. (2019). Impacts of day-ahead versus real-time market prices on wholesale electricity demand in Texas. Energy Economics. 81, 259-272.
  18. Leisen, F. Mena, R.H., Palma-Mancilla, F. and Rossini, L. (2019). On a flexible construction of a negative binomial model. Statistics and Probability Letters. 152, 1-8.
  19. Gutiérrez, L., Gutiérrez-Peńa, E and Mena, R.H., (2019). A Bayesian approach to statistical shape analysis via the projected normal distribution. Bayesian Analysis. 14(2), 427-447.
  20. Fuentes-García, R., Mena, R.H. and Walker, S.G. (2019). Modal posterior clustering motivated by Hopfield’s network. Computational Statistics and Data Analysis. 137, 92-100.
  21. Anzarut, M. and Mena, R. H., (2019). Harris process to model stochastic volatility. Econometrics and Statistics. 10, 151-169.
  22. Anzarut, M., Nava, C., Mena, R. H., and Prünster, I. (2018). Poisson Driven Stationary Markov Models. Journal of Business and Economic Statistics. 36, 684-694.
  23. Gutiérrez, L., Mena, R.H., Ruggiero, M. (2016). A time dependent Bayesian nonparametric model for air quality analysis. Computational Statistics and Data Analysis. 95, 161-175.
  24. Mena, R.H., Ruggiero, M. (2016). Dynamic density estimation with diffusive Dirichlet mixtures. Bernoulli. 22, 901-926.
  25. Mena, R.H. and Walker, S.G. (2015). On the Bayesian mixture model and identifiability Journal of Computational and Graphical Statistics. 24, 1155-1169.
  26. Coen, A. and Mena, R.H. (2015). Ruin probabilities for Bayesian exchanegable claims processes. Journal of Statistical Planning and Inference. 166, 102-115.
  27. De Blasi, P., Favaro, S., Lijoi, A., Mena, R., Prünster, I. and Ruggiero, M. (2015). Are Gibbs-type priors the most natural generalization of the Dirichlet process? IEEE Transactions on Pattern Analysis and Machine Intelligence. 37, 212-229. (pdf)
  28. Martinez, A.F. and Mena, R. (2014). On a nonparametric change point detection model in Markovian regimes. Bayesian Analysis. 9, 823-858.
  29. Gutiérrez, L., Gutiérrez-Peńa, E and Mena, R.H., (2014). Bayesian nonparametric classification for spectroscopy data. Computational Statistics and Data Analysis. 78, 56-68.
  30. Mena, R.H. and Walker, S.G. (2012). An EPPF from independent sequences of geometric random variables. Statistics and Probability Letters. 82, 1059-1066.
  31. Mena, R.H., Ruggiero, M. and Walker, S.G. (2011). Geometric stick-breaking processes for continuous-time Bayesian nonparametric modeling. Journal of Statistical Planning and Inference. 141, 3217-3230. (pdf)
  32. Fuentes-García, R., Mena, R.H. and Walker, S.G. (2010). A probability for classification based on the mixture of Dirichlet process model. Journal of Classification . 27, 389-403.(pdf)
  33. Mena, R.H. and Nieto-Barajas, L. E. (2010). Exchangeble claim sizes in a compound Poisson type process. Applied Stochastic Models in Business and Industry. 26, 737-757. (pdf)
  34. Fuentes-García, R., Mena, R.H. and Walker, S.G. (2010). A new Bayesian nonparametric mixture model. Communications in Statistics-Simulation and Computation. 39, 669-682.(pdf)
  35. Mena, R.H. and Walker, S.G. (2009). On a construction of Markov models in continuous time. Metron. 67, 303-323. (pdf)
  36. Favaro, S., Lijoi, A., Mena, R.H. and Prünster, I. (2009). Bayesian nonparametric inference for species variety with two parameter Poisson-Dirichlet process prior. Journal of the Royal Statistical Society Series B. 71, 993-1008. (pdf)
  37. Fuentes-García, R., Mena, R.H. and Walker, S.G. (2009). A nonparametric dependent process for Bayesian regression. Statistics and Probability Letters. 79, 1112-1119. (pdf)
  38. Contreras-Cristán, A., Mena, R.H. and Walker, S.G. (2009). On the construction of stationary AR(1) models via Bayesian nonparametric predictive distributions. Statistics: A Journal of Theoretical and Applied Statistics. 43, 227-240. (pdf)
  39. Lijoi, A., Mena, R.H., Prünster, I. (2008). A Bayesian nonparametric approach for comparing clustering structures in EST libraries. Journal of Computational Biology, 15, 1315-1327. (pdf)
  40. Lijoi, A., Mena, R.H. and Prünster, I. (2007). A Bayesian nonparametric method for prediction in EST analysis. BMC Bioinformatics. 8:339. (pdf)
  41. Mena, R.H. and Walker, S.G. (2007). Stationary Mixture Transition Distribution (MTD) models via predictive distributions. Journal of Statistical Planning and Inference, 137, 3103-3112. (pdf)
  42. Lijoi, A., Mena, R.H. and Prünster, I. (2007). Controlling the reinforcement in Bayesian nonparametric mixture models. Journal of the Royal Statistical Society Series B. 69, 715-740. (pdf)
  43. Mena, R.H. and Walker, S.G. (2007). On the stationary version of the generalized hyperbolic ARCH model. Annals of the Institute of Statistical Mathematics, 59, 325-348. (pdf)
  44. Lijoi, A., Mena, R.H. and Prünster, I. (2007). Bayesian nonparametric estimation of the probability of discovering new species. Biometrika. 94, 769-786. (pdf)
  45. Escarela, G., Mena, R.H. and Castillo-Morales, A. (2006). A flexible class of parametric transition regression models based on copulas: application to poliomyelitis incidence.  Statistical Methods in Medical Research, 15, 593-609.
  46. Mena, R.H. and Walker, S.G. (2005). Stationary autoregressive models via a Bayesian nonparametric approach. Journal of Time Series Analysis, 26, 789-805. (pdf)
  47. Lijoi, A., Mena, R.H. and Prünster, I. (2005). Bayesian nonparametric analysis for a generalized Dirichlet process prior. Statistical Inference for Stochastic Processes, 8, 283-309. (pdf)
  48. Lijoi, A., Mena, R.H. and Prünster, I. (2005). Hierarchical mixture modelling with normalized inverse Gaussian priors. Journal of the American Statistical Association, 100, 278-1291. (pdf)
  49. Mena, R.H. and Walker, S.G. (2004). A density function connected with a non-negative self-decomposable random variable. Journal of Statistical Computation and Simulation, 74, 765-775. (pdf)

Book chapters

  1. Rodríguez, C.E. and Mena, R.H. (2023). GStatistical modeling to understand the COVID-19 pandemic. In Mathematical Modelling, Simulations, and AI for Emergent Pandemic Diseases: Lessons Learned from COVID-19. Hernandez-Vargas, E. and Velasco-Hernandez, J. X. Eds. Academic Press. ISBN: 9780323950640.
  2. Mena, R.H. (2013). Geometric Weight Priors and their Applications in Bayesian Nonparametrics. In Bayesian Theory and Applications. Damien, P., Dellaportas, P., Polson, N.G. and Stephen, D.A. Eds. Oxford University Press. ISBN 10: 0199695601.
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Proceedings

  1. Gutierrez Inostroza, L., Mena, R.H and Ruggiero, M. (2016). On GEM diffusive mixtures. In JSM Proceedings 2016, Section on Nonparametric Statistics. Alexandria, VA: American Statistical Association.
  2. Nava, C., Mena, R.H and Prünster, I. (2013). On some stationary models: Construction and Estimation. In "The Contribution of Young Researchers to Bayesian Statistics: Proceedings of BAYSM2013." (Springer Proceedings in Mathematics & Statistics 2013), 7pp.
  3. Nava, C., Mena, R.H and Prünster, I. (2013). On Stationary Markov Models: a Poisson-driven approach. In "Proceedings of the 8th Conference on Statistical Computing and Complex Systems - SCo 2013." (electronic), 6pp.
  4. De Blasi, P., Favaro, S., Lijoi, A., Mena, R.H. and Prünster, I. (2012). Two tales about Bayesian nonparametric modeling. In "JSM Proceedings 2012: Section of Bayesian Statistical Science" (Alexandria, VA: American Statistical Association), 11pp.
  5. Favaro, S., Lijoi, A., Mena, R.H. and Prünster, I. (2011). On some issues related to species sampling problems. In "Proceedings of the 7th Conference on Statistical Computing and Complex Systems - SCo 2011" (Padova, 19-21 September 2011), 9pp. ISBN 978-88-6129-753-1
  6. Mena, R.H. and Prünster, I. (2007). Alcune considerazioni sulle elezioni presidenziali messicane del 2006. SIS Magazine. (Electronic)
  7. Lijoi, A., Mena, R.H. and Prünster, I. (2006). Bayesian clustering in nonparametric hierarchical mixture models. Proceedings of XLIII Meeting of the Italian Statistical Society, Vol. I, 449-460.
  8. Escarela, G., Mena, R.H. and Castillo-Morales, A. (2006). Modelling Non-Gaussian Time Series with a Mixture Copula Transition Model. Proceedings of the 21st International Workshop on Statistical Modelling. Galway, Irland, pp. 164-171.
  9. Contreras-Cristán, A., Mena, R.H. and Walker, S.G. (2006). Acerca de la construcción de modelos AR(1) utilizando densidades predictivas que emergen de la estadística Bayesiana no paramétrica. Memoria del XX foro nacional de estadística INEGI-AME. 17-24.
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Other publications

  1. Mena, R.H. (2023). Ramsés H. Mena's Contribution to the Discussion of “Martingale posterior distributions” by Edwin Fong, Chris Holmes and Stephen G. Walker. Journal of the Royal Statistical Society-Series B. 2023; qkad100.
  2. Gil-Leyva, M.F. and Mena, R.H. (2022). Discussion of "Evaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics" by R. Giordano, R. Liu, M. I. Jordan and T. Broderick. Bayesian Analysis. In press.
  3. Lijoi, A., Mena, R.H. and Prünster, I. (2017). Discussion of "Sparse graphs using exchangeable random measures" by F. Caron and E. Fox. Journal of the Royal Statistical Society-Series B. 79(5), 1295-1366.
  4. Mena, R.H. (2014). Book Review "Principles of uncertainty by J.K Kadane (2011). Texts in Statistical Science Series, Chapman and Hall/CRC". Journal of Classification. Vol. 31, No. 2, p. 270-271.
  5. Mena, R.H. (2003). Lenguaje de programación matricial Ox. DATOS Boletín de la Asociación Mexicana de Estadística. 25.
  6. Mena, R.H. (2003). Un ejemplo en Ox. DATOS Boletín de la Asociación Mexicana de Estadística. 26.
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Books (edited volumes)

  1. Hernández-Hernández, D. and Leonardi, F. and Mena, R.H. and Pardo Millán, J.C. (Eds). (2021) Advances in Probability and Mathematical Statistics. Progress in Probability series by Birkhäuser. ISBN 978-3-030-85324-2
  2. Antoniano-Villalobos, I., Mena, R. H., Mendoza, M., Naranjo, L. and Nieto-Barajas, L. E. (Eds). (2019) Selected Contributions on Statistics and Data Science in Latin America. Springer Proceedings in Mathematics and Statistics, Vol 301. ISSN 2194-1009, ISBN 978-3-030-31550-4
  3. Mena, R.H. and Pardo, J.C. and Rivero, V. and Bravo, G.U. (Eds). (2015) XI Symposium on Probability and Stochastic Processes. Progress in Probability series by Birkhäuser. ISBN 978-3-319-13983-8
  4. Hernández, D.C. and Mena, R.H. (Eds). (2013) Volumen en conmemoración del Ańo Internacional de Estadística. Boletín de la Sociedad Matemática Mexicana, Vol. 19, no. 2. ISSN: 2296--4495.
  5. Contreras-Cristán, A., Domínguez-Molina, J.A., Estrada, E. and Mena, R.H. (Eds) (2005) Memorias del XX Foro Nacional de Estadística. INEGI. ISBN 978-13-5055-3
  6. Estrada, E., Martínez, A.F., Mena, R.H. and Nieto-Barajas, L. E. (Eds) (2007) Memorias del XXII Foro Nacional de Estadística. INEGI. ISBN 970-13-4692-0
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My research has been supported by

conacyt dgapa ame isba
carlo alberto banff
bernoulli decastro iimas
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