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A bayesian nonparametric approach for the two-sample problem
(Universidade Federal de São CarlosUFSCarPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEsCâmpus São Carlos, 2018-11-19)
In this work, we discuss the so-called two-sample problem (PEARSON; NEYMAN, 1930)
assuming a nonparametric Bayesian approach. Considering X 1 ,...,X n and Y 1 ,...,Y m two inde-
pendent i.i.d samples generated from P 1 ...
Computational aspects of nonparametric Bayesian analysis with applications to the modeling of multiple binary sequences
(AMER STATISTICAL ASSOC, 2000)
We consider Markov mixture models for multiple longitudinal binary sequences. Prior uncertainty in the mixing distribution is characterized by a Dirichlet process centered on a matrix beta measure. We use this setting to ...
Semiparametric Bayesian classification with longitudinal markers
(BLACKWELL PUBLISHING, 2007)
We analyse data from a study involving 173 pregnant women. The data are observed values of the beta human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six ...
A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem
(TAYLOR & FRANCIS INC, 2010)
In this article, we introduce a semi-parametric Bayesian approach based on Dirichlet process priors for the discrete calibration problem in binomial regression models. An interesting topic is the dosimetry problem related ...
A Bayesian semi-parametric approach to the ordinal calibration problem
(Taylor & Francis, 2016)
We introduce a semi-parametric Bayesian approach based on skewed Dirichlet processes priors for location parameters in the ordinal calibration problem. This approach allows the modeling of asymmetrical error distributions. ...
A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem
(TAYLOR & FRANCIS INC, 2010)
In this article, we introduce a semi-parametric Bayesian approach based on Dirichlet process priors for the discrete calibration problem in binomial regression models. An interesting topic is the dosimetry problem related ...
Theory and computations for the dirichlet process and related models: an overview
(2017)
Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished ...
Semiparametric Bayesian measurement error modeling
(ELSEVIER INC, 2010)
This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a ...
Semiparametric Bayesian measurement error modeling
(ELSEVIER INC, 2010)
This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a ...
Nonparametric Bayesian modeling for multivariate ordinal data
(AMER STATISTICAL ASSOC, 2005)
This article proposes a probability model for kappa-dimensional ordinal outcomes, that is, it considers inference for data recorded in kappa-dimensional contingency tables with ordinal factors. The proposed approach is ...