Browsing by Author "Gutierrez, Luis"
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- ItemA Bayesian Nonparametric Multiple Testing Procedure for Comparing Several Treatments Against a Control(2019) Gutierrez, Luis; Barrientos, Andres F.; Gonzalez, Jorge; Taylor-Rodriguez, DanielWe propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters. In contrast, our approach identifies differences across the entire distribution, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposal is compared to other commonly used hypothesis testing procedures under simulated scenarios. Two real applications are also analyzed with the proposed methodology.
- ItemA new flexible Bayesian hypothesis test for multivariate data(2023) Gutierrez, Ivan; Gutierrez, Luis; Alvares, DaniloWe propose a Bayesian hypothesis testing procedure for comparing the multivariate distributions of several treatment groups against a control group. This test is derived from a flexible model for the group distributions based on a random binary vector such that, if its jth element equals one, then the jth treatment group is merged with the control group. The group distributions' flexibility comes from a dependent Dirichlet process, while the latent vector prior distribution ensures a multiplicity correction to the testing procedure. We explore the posterior consistency of the Bayes factor and provide a Monte Carlo simulation study comparing the performance of our procedure with state-of-the-art alternatives. Our results show that the presented method performs better than competing approaches. Finally, we apply our proposal to two classical experiments. The first one studies the effects of tuberculosis vaccines on multiple health outcomes for rabbits, and the second one analyzes the effects of two drugs on weight gain for rats. In both applications, we find relevant differences between the control group and at least one treatment group.
- ItemFactors associated to the duration of COVID-19 lockdowns in Chile(2022) Pavani, Jessica; Cerda, Jaime; Gutierrez, Luis; Varas, Ines; Gutierrez, Ivan; Jofre, Leonardo; Ortiz, Oscar; Arriagada, GabrielDuring the first year of the COVID-19 pandemic, several countries have implemented non-pharmacologic measures, mainly lockdowns and social distancing, to reduce the spread of the SARS-CoV-2 virus. These strategies varied widely across nations, and their efficacy is currently being studied. This study explores demographic, socioeconomic, and epidemiological factors associated with the duration of lockdowns applied in Chile between March 25th and December 25th, 2020. Joint models for longitudinal and time-to-event data were used. In this case, the number of days under lockdown for each Chilean commune and longitudinal information were modeled jointly. Our results indicate that overcrowding, number of active cases, and positivity index are significantly associated with the duration of lockdowns, being identified as risk factors for longer lockdown duration. In short, joint models for longitudinal and time-to-event data permit the identification of factors associated with the duration of lockdowns in Chile. Indeed, our findings suggest that demographic, socioeconomic, and epidemiological factors should be used to define both entering and exiting lockdown.
- ItemLinear models for statistical shape analysis based on parametrized closed curves(2020) Gutierrez, Luis; Mena, Ramses H.; Diaz-Avalos, CarlosWe develop a simple, yet powerful, technique based on linear regression models of parametrized closed curves which induces a probability distribution on the planar shape space. Such parametrization is driven by control points which can be estimated from the data. Our proposal is capable to infer about the mean shape, to predict the shape of an object at an unobserved location, and, while doing so, to consider the effect of predictors on the shape. In particular, the model is able to detect possible differences across the levels of the predictor, thus also applicable for two-sample tests. A simple MCMC algorithm for Bayesian inference is also presented and tested with simulated and real datasets. Supplementary material is available online.
- ItemMultivariate Bayesian discrimination for varietal authentication of Chilean red wine(TAYLOR & FRANCIS LTD, 2011) Gutierrez, Luis; Quintana, Fernando A.; von Baer, Dietrich; Mardones, ClaudiaThe process through which food or beverages is verified as complying with its label description is called food authentication. We propose to treat the authentication process as a classification problem. We consider multivariate observations and propose a multivariate Bayesian classifier that extends results from the univariate linear mixed model to the multivariate case. The model allows for correlation between wine samples from the same valley. We apply the proposed model to concentration measurements of nine chemical compounds named anthocyanins in 399 samples of Chilean red wines of the varieties Merlot, Carmenere and Cabernet Sauvignon, vintages 2001-2004. We find satisfactory results, with a misclassification error rate based on a leave-one-out cross-validation approach of about 4%. The multivariate extension can be generally applied to authentication of food and beverages, where it is common to have several dependent measurements per sample unit, and it would not be appropriate to treat these as independent univariate versions of a common model.
- ItemMULTIVARIATE BAYESIAN SEMIPARAMETRIC MODELS FOR AUTHENTICATION OF FOOD AND BEVERAGES(INST MATHEMATICAL STATISTICS, 2011) Gutierrez, Luis; Quintana, Fernando A.Food and beverage authentication is the process by which foods or beverages are verified as complying with its label description, for example, verifying if the denomination of origin of an olive oil bottle is correct or if the variety of a certain bottle of wine matches its label description. The common way to deal with an authentication process is to measure a number of attributes on samples of food and then use these as input for a classification problem. Our motivation stems from data consisting of measurements of nine chemical compounds denominated Anthocyanins, obtained from samples of Chilean red wines of grape varieties Cabernet Sauvignon, Merlot and Carmenere. We consider a model-based approach to authentication through a semiparametric multivariate hierarchical linear mixed model for the mean responses, and covariance matrices that are specific to the classification categories. Specifically, we propose a model of the ANOVA-DDP type, which takes advantage of the fact that the available covariates are discrete in nature. The results suggest that the model performs well compared to other parametric alternatives. This is also corroborated by application to simulated data.
- ItemOn a Dirichlet Process Mixture Representation of Phase-Type Distributions(2022) Ayala, Daniel; Jofre, Leonardo; Gutierrez, Luis; Mena, Ramses H.An explicit representation of phase-type distributions as an infinite mixture of Erlang distributions is introduced. The representation unveils a novel and useful connection between a class of Bayesian nonparametric mixture mod-els and phase-type distributions. In particular, this sheds some light on two hot topics, estimation techniques for phase-type distributions, and the availability of closed-form expressions for some functionals related to Dirichlet process mixture models. The power of this connection is illustrated via a posterior inference al-gorithm to estimate phase-type distributions, avoiding some difficulties with the simulation of latent Markov jump processes, commonly encountered in phase-type Bayesian inference. On the other hand, closed-form expressions for functionals of Dirichlet process mixture models are illustrated with density and renewal function estimation, related to the optimal salmon weight distribution of an aquaculture study.