Día: 19/06/2019
Horario: 16h30 a las 17h30
OC 1.1 – POISSON MODEL FOR UNDERREPORTED DATA USING AUXILIARY CLUSTERING VARIABLES Guilherme Oliveira, Departamento de Computação, Centro Federal de Educação Tecnológica, Brazil.
OC 1.2 – A BETA INFLATED MEAN REGRESSION MODEL WITH MIXED EFFECTS FOR FRACTIONAL RESPONSE VARIABLES Renzo Fernández, PUCP, Perú.
OC 1.3 – SPATIAL REGRESSION MODELS FOR BOUNDED RESPONSE VARIABLES WITH DIFFERENT DEPENDENCE STRUCTURE Sandra Elizabeth Flores, IME, USP, Brazil.
OC 2.1 – ON CHOOSING MIXTURE COMPONENTS VIA NON-LOCAL PRIORS Jairo Alberto Fuquene, Bloomberg Philanthropies Data for Health Initiative, Colombia.
OC 2.2 – ELICITATION OF THE PARAMETERS OF THE MULTIVARIATE LINEAR MODEL Carlos Javier Barrera,Metropolitan Technological Institute, Medellín,Colombia.
OC 2.3 – METHOD TO OBTAIN A VECTOR OF HYPERPARAMETERS: APPLICATION IN BERNOULLI TRIALS Llerzy Esneider Torres, Universidad del Valle, Colombia.
OC 2.4 – METHOD OF ELICITATING PRIOR DISTRIBUTION FOR THE DEPENDENCE PARAMETER OF COPULA Jose Rafael Tovar, Universidad del Valle, Colombia.
OC 3.1 – MEXICAN RESTAURANT PROCESSES Arrigo Coen, Universidad Nacional Autónoma de México, México.
OC 3.2 – A MULTIVARIATE PROJECTED GAMMA MODEL FOR DIRECTIONAL DATA Emiliano Geneyro Squarzon, Universidad Autónoma Metropolitana-Iztapalapa, México.
OC 3.3 – TIME-DEPENDENT RANDOM PROBABILITY MEASURES BASED ON RANDOM SETS María Fernanda Gil Leyva, Universidad Nacional Autónoma de México, México.
CO 3.4 – BAYESIAN NONPARAMETRIC MODELS OF DIRECTIONAL VARIABLES BASED ON DIRICHLET PROCESS MIXTURES OF GAMMA DISTRIBUTIONS Gabriel Núñez Antonio, UAM-I, Mexico.
Día: 20/06/2019
Horario: 16h30 a las 17h30
OC 4.1 – DETECTION OF CHANGE POINTS IN AIR CONTAMINATION BY PM10 IN THE CITY OF BOGOTÁ Biviana Marcela Suárez, Universidad Nacional de Colombia, Colombia.
OC 4.2 – EXACT BAYESIAN INFERENCE FOR MARKOV SWITCHING COX PROCESSESLivia Maria Dutra, Departamento de Estatística, Universidade Federal de Minas Gerais, Minas Gerais, Brazil
OC 4.3 – MIXED EFFECTS STATE-SPACE MODELS WITH STUDENT-T ERRORS Lina Lucia Hernandez, UFRJ, Brazil.
OC 4.4 – DYNAMIC TIME-SERIES CLUSTERING Victhor Simões, UFRJ, Brazil.
OC 5.1 – PENALIZED BAYESIAN D-OPTIMAL DESIGNS FOR TWO-PARAMETER EXPONENTIAL REGRESSION MODEL Svetlana Ivanovna Rudnykh, Universidad Nacional de Colombia, Colombia.
OC 5.2 – Performance of the X control chart with predictive limits Isabel Cristina Ramírez, Escuela de Estadística, Universidad Nacional de Colombia, Medellín, Colombia.
OC 5.3 – SAMPLE SIZE FOR ACCEPTATION SAMPLING USING FREQUENTIST AND BAYESIAN APPROACH Cristian David Correa, Universidad Nacional de Colombia, Colombia.
OC 5.4 – A BAYESIAN ANALYSIS OF THE MATCHING PROBLEM Ignacio Vidal, Universidad de Talca, Talca, Chile
OC 6.1 – STOCHASTIC VOLATILITY MODELS USING HAMILTONIAN MONTE CARLO METHODS AND STAN Ricardo S. Ehlers, Instituto de Ciencias Matematicas e de Computação, Universidade de São Paulo, São Carlos, SP, Brazil.
OC 6.2 – GAUSSIAN COPULA IN A STOCHASTIC VOLATILITY MODEL WITH SMOOTH TRANSITION REGIMES William Lima Leão, UFRJ, Brazil.
OC 6.3 – ESTIMATING AND FORECASTING DEMAND ELECTRICITY FOR RESIDENTIAL SECTOR IN COSTA RICAN ELECTRICITY INSTITUTE. COMPARISON OF A BAYESIAN AND FREQUENTIST MODEL Marco Otoya, Universidad de Costa Rica.
OC 6.4 – BAYESIAN MIXED GENERALIZED LINEAR MODEL OF ECONOMIC SENSITIZATION FOR BANKING CREDIT PORTFOLIO UNDER THE RISK ANALYSIS APPROACH Melissa Cordero, Universidad de Costa Rica, Costa Rica.
Día 21/06/2019
Horario: 14h30 a las 15h30
OC 7.1 – TENSOR ON TENSOR REGRESSION WITH VARIOUS LOW-RANK REGRESSION PARAMETERS AND CONJUGATE PRIORS Carlos Jonathan Llosa, Iowa State University, USA.
OC 7.2 – A LATENT SPACE MODEL FOR COGNITIVE SOCIAL STRUCTURES DATA Juan Sosa, Universidad Externado de Colombia, Colombia.
OC 7.3 – IS SMERED+ ALGORITHM GENERATING LAZY MARKOV CHAINS?Paula Andrea Bran, Universidad del Valle, Cali, Colombia.
OC 7.4 – GAUSSIAN PROCESS VOLATILITY MODEL INFERENCE WITH PARTICLE LEARNING ALGORITHM Daniel Cunha Oliveira, IME, USP, Brazil.
Día: 21/06/2019
Horario: 15h45 a las 16h30
OC 8.1 – GEOSTATISTICS UNDER PREFERENTIAL SAMPLING IN THE PRESENCE OF LOCAL REPULSION EFFECTS Gustavo da Silva Ferreira, Ence, IBGE, Brazil.
OC 8.2 – ABOUT THE SPECIFICATION OF THE NEIGHBORHOOD STRUCTURE IN THE NEAREST NEIGHBOR GAUSSIAN PROCESS Mariana del Pilar Lizarazo, UFRJ, Brazil.
OC 8.3 – BAYESIAN BLOCK NEAREST NEIGHBOR GAUSSIAN PROCESS FOR LARGE GEOSTATISTICAL DATA Zaida Quiroz Cornejo, PUCP, Perú.
OC 9.1 – INTERVAL-CENSORED DATA WITH MISCLASSIFICATION: A BAYESIAN APPROACH Guilherme Augusto Veloso, Departamento de Estatística, Universidade Federal de Minas Gerais, Minas Gerais, Brazil.
OC 9.2 – BAYESIAN SURVIVAL MODEL FOR LIFETIME WITH LONG-TERM SURVIVORS IN PRESENCE OF UNOBSERVED HETEROGENEITY Vicente Garibay, Instituto de Ciencias Matematicas e de Computação, Universidade de São Paulo, São Carlos, SP, Brazil.
OC 9.3 – FULLY BAYESIAN ANALYSIS OF ALLELE-SPECIFIC RNA-SEQ DATA Ignacio Alvarez, Universidad de la República, Uruguay.
OC 10.1 – LONGITUDINAL MULTIDIMENSIONAL ITEM RESPONSE MODELLING IN PRESCHOOL CHILDREN’S MENTAL STATE UNDERSTANDING Vilma Susana Romero, Department of Mathematics and Statistics, Lancaster University, UK.
OC 10.2 – BAYESIAN LINEAR REGRESSION MODELS WITH FLEXIBLE ERROR DISTRIBUTIONS Nívea Bispo, Center for Integration of Data and Health Knowledge – CIDACS/Fiocruz-BA, Salvador, Brazil.
OC 10.3 – A CALIBRATION OF A REASONING TEST USING BAYESIAN ITEM RESPONSE THEORY Guaner Rojas, Universidad de Costa Rica, Costa Rica.