Viana, A. T.; Gonçalves, K. C. M.; Paez, M. S . Factor model for ordinal categorical data with latent factors explained by auxiliary variables applied to the major depression inventory. Journal of Applied Statistics, v. 1, p. 1-28, 2024. https://doi.org/10.1080/02664763.2024.2321913
Souza, R., Costa, L.C., Paez, M.S., Sato, J. e Barreto, C. Dynamic Graphical Models with Variable Selection for Effective Connectivity. Bayesian Analysis. Advance Publication 1-25. 2023. DOI: 10.1214/23-BA1377.
Amini, A., Paez, M. S., Lin, L. Hierarchical stochastic block model for community detection in multiplex networks. Bayesian Analysis. Advance Publication 1-27, 2022. DOI: 10.1214/22-BA1355.
Morales, F. E. C.; Politis, D. N.; Leskow, J.; Paez, M. S. ``Student’s-t process with spatial deformation for spatio-temporal data''. Statistical Methods and Applications, v. 31, p.1099–1126 , 2022.
Moura, P. H., Godoy, P. H.; Salis, L. H. A.; Paez, M. S.; Barreto, D. W.; Maia, L. F. P. G.; Silva, N. A. S. Qualidade do ar e índice de progresso social em bairros do Rio de Janeiro, um alerta à saúde. Revista Ibero-americana de Ciências Ambientais, v. 12, n. 6, 2021.
Paez, M.S, Walker, S. G. Modeling with a large class of unimodal multivariate distributions. Journal of Applied Statistics, v. 45, p. 1-23, 2018.
Cunha, M. G.; Gamerman, D.; Fuentes, M.; Paez, M. A non-stationary spatial model for temperature interpolation applied to the state of Rio de Janeiro. Journal of the Royal Statistical Society Series C - Applied Statistics, v. 1, p. 1, 2017.
Pinto Jr, J. A.; Gamerman, D.; Paez, M. S. ; Fonseca, R. H. Point pattern analysis with spatially varying covariate effects, applied to the study of cerebrovascular deaths. Statistics in Medicine (Print), v. 34, p. 1214-1226, 2015.
Ávila, M. A. P.; Borges, L. P.; Paez, M. S.; Bruno, R. V.; Nardi, A. E.; Pessôa, A. C. M.; Palmeira, E. S . Acantose nigricante: inter-relações metabólicas inerentes à síndrome dos ovários policísticos. Revista Brasileira de Ginecologia e Obstetrícia (Impresso), v. 36, p. 410-415, 2014.
Segenreich, D.; Paez, M. S.; Regalla, M. A.; Fortes, D.; Faraone, S. V.; Sergeant, J.; Mattos, P. Multilevel analysis of ADHD, anxiety and depression symptoms aggregation in families. European Child & Adolescent Psychiatry, v. 2, p. 1435, 2014.
Reis, E. A.; Gamerman, D.; Paez, M. S.; Martins, T. G. Bayesian dynamic models for space-time point processes. Computational Statistics & Data Analysis (Print), v. 60, p. 146-156, 2013.
Castro Morales, F. E. ; Gamerman, D.; Paez, M. S. State space models with spatial deformation. Environmental and Ecological Statistics, v. 20, p. 191-214, 2013.
Paez, M. S.; Diggle, P.. Cox processes for estimating temporal variation in disease risk. EnvironMetrics (London), v. 20, p. 981-1003, 2009.
Paez, M. S.; Gamerman, D.; Landim, F. M. F. P. ; Salazar, E. . Spatially varying dynamic coefficient models. Journal of Statistical Planning and Inference, v. 138, p. 1038-1058, 2008.
Carvalho, R. S. ; Migon, H. S. ; Paez, M. S. Dynamic Bayesian models as an alternative to the estimation of operational risk measures. The Journal of Operational Risk (Online), v. 3, p. 25-49, 2008.
Paez, M. S.; Gamerman, D.; Oliveira, Victor de . Interpolation performance of a spatio-temporal model with spatially-varying coefficients: application to PM10 concentrations in Rio de Janeiro. Environmental and Ecological Statistics, v. 12, p. 169-193, 2005.
Paez, M. S.; Gamerman, D. Study of the space-time effects in the concentration of airborne pollutants in the Metropolitan Region of Rio de Janeiro. EnvironMetrics (London), Canada, v. 14, p. 387-408, 2003.
Szwarcwald, C. L.; Esteves, M. A. P.; Andrade, C. L. T.; Paez, M. S.; Medici, E. V. ; Derrico, M . Desigualdade de Renda e Situação de Saúde: O Caso do Rio de Janeiro. Cadernos de Saúde Pública (FIOCRUZ), Rio de Janeiro, v. 15, n.1, p. 15-25, 1999.
Paez, M. S.; Gamerman, D. Modelagem de Processos espaço-temporais. 1. ed. Vitória: Gráfica Universitária, 2005. 101p .
Paez, M. S.; Gamerman, D. Dynamic Hierarchical Linear Models. In: Jeffrey S. Simonoff, Marc A. Scott, Brian P. Marx (Org.). The Sage Handbook of Multilevel Modeling. 1ed.Londres, U.K.: SAGE Publications Ltd, 2012, v. 1, p. 1-620.
Paez, M. S. Bayesian Techniques. In: Helcio R.B. Orlande; Olivier Fudym; Denis Maillet; Renato M. Cotta. (Org.). Thermal Measurements and Inverse Techniques. 1ed.: CRC Press (Taylor and Francis Group), 2011, v. 1, p. 1-770.