Boletim do resumo e programas (XIV EMR 2015)

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XIV Escola de Modelos de Regressão De 2 a 5 de Março de 2015 - Centro de Convenções - Unicamp 13 Conferência 6 (CF6) Box-Cox symmetric models and applications to nutritional data Silvia L. P. Ferrari Departmento de Estatística – Universidade de São Paulo, Brasil Resumo: We introduce and study the Box-Cox symmetric class of distributions, which is useful for modeling positively skewed, possibly heavy-tailed, data. The new class of distributions includes the Box-Cox t, Box-Cox Cole-Green, Box-Cox power exponential distributions, and the class of the log-symmetric distributions as special cases. It provides easy parameter interpretation, which makes it convenient for regression modeling purposes. Additionally, it provides enough flexibility to handle outliers. The usefulness of the Box-Cox symmetric models is illustrated in a series of applications to nutritional data. Joint work with Giovana Fumes. Data e Horário Março 4 10: 30 ate 11: 30 Auditório 2 : emrxiv@gmail.com : http://www.ime.usp.br/~abe/emr2015
XIV Escola de modelos de Regressão 14 De 2 a 5 de Março de 2015 - Centro de Convenções - Unicamp Conferência 7 (CF7) Bayesian sparse reduced rank multivariate regression Dipak Dey University of Connecticut, Storrs, USA Resumo: Many modern statistical problems can be cast in the framework of multivariate regression, where the main task is the estimation of a possibly highdimensional coefficient matrix. The low-rank structure in the coefficient matrix is of intrinsic multivariate nature, which, when further combined with sparsity, can further lift dimension reduction, conduct variable selection, and facilitate model interpretation. Using a Bayesian approach, we develop a unified sparse and lowrank multivariate regression method, to both estimate the coefficient matrix and obtain its credible region for making inference. The newly developed sparsityinducing prior for the coefficient matrix enables simultaneous rank reduction, predictor selection, as well as response selection. We utilize the marginal likelihood to determine the regularization hyperparameter, so it maximizes its posterior probability given the data. Theoretically, the posterior consistency is established under a high-dimensional asymptotic regime. The efficacy of the proposed approach is demonstrated via simulation studies and a real application on yeast cell cycle data. Keywords: Bayesian; Low rank; Penalized least squares; Posterior consistency; Sparsity; Rank Joint with Gyuhyeong Goh, and Kun Chen. Data e Horário Março 4 11:30 ate 12:30 Auditório 1 : emrxiv@gmail.com : http://www.ime.usp.br/~abe/emr2015
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