Publications in refereed journals
- Rodríguez, C.E. and Mena, R.H. and Walker, S.G. (2024). Martingale posterior inference for finite mixture models and clustering. Journal of Computational and Graphical Statistics. In press.
- Acuña-Zegarra, M., Santana-Cibrian, M., Rodríguez, C.E., Mena, R.H. and Velasco-Hernández, J. (2024). On the estimation of partially observed continuous--time Markov chains. A retrospective analysis of COVID-19 dynamics in Mexico and Peru: Studying hypothetical changes in the contact rate. 793, 229-250
- 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
- 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.
- 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
- 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.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Gutiérrez, L., Gutiérrez-Peña, E and Mena, R.H., (2019). A Bayesian approach to statistical shape
analysis via the projected normal distribution. Bayesian Analysis. 14(2), 427-447.
- 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.
- Anzarut, M. and Mena, R. H., (2019). Harris process to model stochastic volatility. Econometrics and Statistics. 10, 151-169.
- 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.
- 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.
- Mena, R.H., Ruggiero, M. (2016). Dynamic density estimation with diffusive Dirichlet mixtures. Bernoulli. 22, 901-926.
- Mena, R.H. and Walker, S.G. (2015). On the Bayesian mixture model and identifiability Journal of Computational and Graphical Statistics. 24, 1155-1169.
- Coen, A. and Mena, R.H. (2015). Ruin probabilities for Bayesian exchanegable claims processes. Journal of Statistical Planning and Inference. 166, 102-115.
- 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)
- Martinez, A.F. and Mena, R. (2014). On a nonparametric change point detection model in Markovian regimes. Bayesian Analysis. 9, 823-858.
- Gutiérrez, L., Gutiérrez-Peña, E and Mena, R.H., (2014). Bayesian nonparametric classification for spectroscopy data. Computational Statistics and Data Analysis. 78, 56-68.
- Mena, R.H. and Walker, S.G. (2012). An EPPF from independent sequences of geometric random variables. Statistics and Probability Letters. 82, 1059-1066.
- 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)
- 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)
- 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)
- 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)
- Mena, R.H. and Walker, S.G. (2009). On a construction of Markov models in continuous time. Metron. 67, 303-323. (pdf)
- 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)
- 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)
- 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)
- 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)
- Lijoi, A., Mena, R.H. and Prünster, I. (2007). A Bayesian nonparametric method for prediction in EST analysis. BMC Bioinformatics. 8:339. (pdf)
- 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)
- 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)
- 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)
- Lijoi, A., Mena, R.H. and Prünster, I. (2007). Bayesian nonparametric estimation of the probability of discovering new species. Biometrika. 94, 769-786. (pdf)
- 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.
- 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)
- 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)
- 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)
- 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
- 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.
- 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.
Proceedings
- 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.
- 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.
- 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.
- 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.
- 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
- Mena, R.H. and Prünster, I. (2007). Alcune considerazioni sulle elezioni presidenziali messicane del 2006. SIS Magazine. (Electronic)
- 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.
- 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.
- 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.
Other publications
- 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.
- 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.
- 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.
- 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.
- Mena, R.H. (2003). Lenguaje de programación matricial Ox. DATOS Boletín de la Asociación Mexicana de Estadística. 25.
- Mena, R.H. (2003). Un ejemplo en Ox. DATOS Boletín de la Asociación Mexicana de Estadística. 26.
Books (edited volumes)
- 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
- 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
- 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
- Hernández, D.C. and Mena, R.H. (Eds). (2013) Volumen en conmemoración del Año Internacional de Estadística. Boletín de la Sociedad Matemática Mexicana, Vol. 19, no. 2. ISSN: 2296--4495.
- 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
- 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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