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Estimação de funções do redshift de galáxias com base em dados fotométricos
(Universidade Federal de São CarlosUFSCarPrograma Interinstitucional de Pós-Graduação em Estatística - PIPGEsCâmpus São Carlos, 2017-09-18)
In a substantial amount of astronomy problems, we are interested in estimating values assumed of some unknown quantity z ∈ R, for many function g, based on covariates x ∈ R^d. This is made using a sample (X1,Z1), ... ...
Nonparametric Bayesian modelling using skewed Dirichlet processes
(ELSEVIER, 2009)
We introduce a new class of discrete random probability measures that extend the definition of Dirichlet process (DP) by explicitly incorporating skewness. The asymmetry is controlled by a single parameter in such a way ...
Semiparametric estimation and inference using doubly robust moment conditions
(2013-12-05)
We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step. The key difference is that we do not impose any parametric restriction on the nuisance ...
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 ...
A Product Partition Model With Regression on Covariates
(AMER STATISTICAL ASSOC, 2011)
We propose a probability model for random partitions in the presence of covariates. In other words, we develop a model-based clustering algorithm that exploits available covariates. The motivating application is predicting ...
A time dependent Bayesian nonparametric model for air quality analysis
(Elsevier, 2016)
Air quality monitoring is based on pollutants concentration levels, typically recorded in
metropolitan areas. These exhibit spatial and temporal dependence as well as seasonality
trends, and their analysis demands flexible ...
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
(JOURNAL STATISTICAL SOFTWARE, 2011)
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 ...
Elicitation of multivariate prior distributions: A nonparametric Bayesian approach
(Elsevier B.V., 2010-07-01)
In the context of Bayesian statistical analysis, elicitation is the process of formulating a prior density f(.) about one or more uncertain quantities to represent a person's knowledge and beliefs. Several different methods ...