Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Spatial distribution of dengue incidence and socio-environmental conditions in Campinas, São Paulo State, Brazil, 2007

This study aimed to analyze the spatial distribution of dengue risk and its association with socio-environmental conditions. This was an ecological study of the counts of autochthonous dengue cases in the municipality of Campinas, São Paulo State, Brazil, in the year 2007, aggregated according to 47 coverage areas of municipal health centers. Spatial models for mapping diseases were constructed with Bayesian hierarchical models, based on Integrated Nested Laplace Approximation (INLA). The analyses were stratified according to two age groups, 0 to 14 years and above 14 years. The results indicate that the spatial distribution of dengue risk is not associated with socio-environmental conditions in the 0 to 14 year age group. In the age group older than 14 years, the relative risk of dengue increases significantly as the level of socio-environmental deprivation increases. Mapping of socio-environmental deprivation and dengue cases proved to be a useful tool for data analysis in dengue surveillance systems.

Modelos bayesianos para análise de características reprodutivas de fêmeas Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Comparison of different nonlinear functions to describe Nelore cattle growth

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Avaliação de modelos estatísticos considerando a incerteza da paternidade

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelos lineares generalizados bayesianos para dados longitudinais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Risk factors for Hospital-Acquired Pneumonia in nonventilated adults

Although most recent publications focus on Ventilator-associated Pneumonia, Non-Ventilator-associated Hospital-acquired pneumonia (NVHAP) is still worrisome. We studied risk factors for NVHAP among patients admitted to a small teaching hospital. Sixty-six NVHAP case patients and 66 controls admitted to the hospital from November 2005 through November 2006 were enrolled in a case-control study. Variables under investigation included: demographic characteristics, comorbidities, procedures, invasive devices and use of medications (Sedatives, Antacids, Steroids and Antimicrobials). Univariate and multivariable analysis (hierarchical models of logistic regression) were performed. The incidence of NVHAP in our hospital was 0.68% (1.02 per 1,000 patients-day). Results from multivariable analysis identified risk factors for NVHAP: age (Odds Ratio[OR]=1.03, 95% Confidence Interval[CI]=1.01-1.05, p=0.002), use of Antacids (OR=5.29, 95%CI=1.89-4.79, p=0.001) and Central Nervous System disease (OR=3.13, 95%CI=1.24-7.93, p=0.02). Although our findings are coherent with previous reports, the association of Antacids with NVHAP recalls a controversial issue in the physiopathology of Hospital-Acquired Pneumonia, with possible implications for preventive strategies.

Accuracy of genomic predictions in Bos indicus (Nellore) cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Modelo dinâmico bayesiano multivariado para análise espaço-temporal de dados de área

Pós-graduação em Matematica Aplicada e Computacional - FCT

Validation of the Geriatric Oral Health Assessment Index in complete denture wearers

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Metodologia de atribuição dos escores visuais e estudo de associação genômica para os escores de conformação, precocidade e musculosidade em bovinos Nelore

Pós-graduação em Genética e Melhoramento Animal - FCAV

Análise espacial do potencial fotovoltaico em telhados de residências usando modelagem hierárquica bayesiana

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Bayesian analysis of random regression models using B-splines to model test-day milk yield of Holstein cattle in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

Bayesian longitudinal data analysis with mixed models and thick-tailed distributions using MCMC

Linear mixed effects models are frequently used to analyse longitudinal data, due to their flexibility in modelling the covariance structure between and within observations. Further, it is easy to deal with unbalanced data, either with respect to the number of observations per subject or per time period, and with varying time intervals between observations. In most applications of mixed models to biological sciences, a normal distribution is assumed both for the random effects and for the residuals. This, however, makes inferences vulnerable to the presence of outliers. Here, linear mixed models employing thick-tailed distributions for robust inferences in longitudinal data analysis are described. Specific distributions discussed include the Student-t, the slash and the contaminated normal. A Bayesian framework is adopted, and the Gibbs sampler and the Metropolis-Hastings algorithms are used to carry out the posterior analyses. An example with data on orthodontic distance growth in children is discussed to illustrate the methodology. Analyses based on either the Student-t distribution or on the usual Gaussian assumption are contrasted. The thick-tailed distributions provide an appealing robust alternative to the Gaussian process for modelling distributions of the random effects and of residuals in linear mixed models, and the MCMC implementation allows the computations to be performed in a flexible manner.