Associação de polimorfismos de nucleotídeo único com características de crescimento e reprodutivas em bovinos Canchim

Pós-graduação em Genética e Melhoramento Animal - FCAV

Parâmetros genéticos e associação de caracteres de importância econômica com polimorfismos dos genes ADIPOR1 e APOB em frangos de corte

Pós-graduação em Genética e Melhoramento Animal - FCAV

Genome-Wide Association for Growth Traits in Canchim Beef Cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Association of apolipoprotein B and adiponectin receptor 1 genes with carcass, bone integrity and performance traits in a paternal broiler line

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Whither PQL?

Generalized linear mixed models (GLMM) are generalized linear models with normally distributed random effects in the linear predictor. Penalized quasi-likelihood (PQL), an approximate method of inference in GLMMs, involves repeated fitting of linear mixed models with “working” dependent variables and iterative weights that depend on parameter estimates from the previous cycle of iteration. The generality of PQL, and its implementation in commercially available software, has encouraged the application of GLMMs in many scientific fields. Caution is needed, however, since PQL may sometimes yield badly biased estimates of variance components, especially with binary outcomes. Recent developments in numerical integration, including adaptive Gaussian quadrature, higher order Laplace expansions, stochastic integration and Markov chain Monte Carlo (MCMC) algorithms, provide attractive alternatives to PQL for approximate likelihood inference in GLMMs. Analyses of some well known datasets, and simulations based on these analyses, suggest that PQL still performs remarkably well in comparison with more elaborate procedures in many practical situations. Adaptive Gaussian quadrature is a viable alternative for nested designs where the numerical integration is limited to a small number of dimensions. Higher order Laplace approximations hold the promise of accurate inference more generally. MCMC is likely the method of choice for the most complex problems that involve high dimensional integrals.

A Nonstationary Negative Binomial Time Series with Time-Dependent Covariates: Enterococcus Counts in Boston Harbor

Boston Harbor has had a history of poor water quality, including contamination by enteric pathogens. We conduct a statistical analysis of data collected by the Massachusetts Water Resources Authority (MWRA) between 1996 and 2002 to evaluate the effects of court-mandated improvements in sewage treatment. Motivated by the ineffectiveness of standard Poisson mixture models and their zero-inflated counterparts, we propose a new negative binomial model for time series of Enterococcus counts in Boston Harbor, where nonstationarity and autocorrelation are modeled using a nonparametric smooth function of time in the predictor. Without further restrictions, this function is not identifiable in the presence of time-dependent covariates; consequently we use a basis orthogonal to the space spanned by the covariates and use penalized quasi-likelihood (PQL) for estimation. We conclude that Enterococcus counts were greatly reduced near the Nut Island Treatment Plant (NITP) outfalls following the transfer of wastewaters from NITP to the Deer Island Treatment Plant (DITP) and that the transfer of wastewaters from Boston Harbor to the offshore diffusers in Massachusetts Bay reduced the Enterococcus counts near the DITP outfalls.

Computational Techniques for Spatial Logistic Regression with Large Datasets

In epidemiological work, outcomes are frequently non-normal, sample sizes may be large, and effects are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. We focus on binary outcomes, with the risk surface a smooth function of space. We compare penalized likelihood models, including the penalized quasi-likelihood (PQL) approach, and Bayesian models based on fit, speed, and ease of implementation. A Bayesian model using a spectral basis representation of the spatial surface provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being more efficient computationally than other Bayesian approaches. One of the contributions of this work is further development of this underused representation. The spectral basis model outperforms the penalized likelihood methods, which are prone to overfitting, but is slower to fit and not as easily implemented. Conclusions based on a real dataset of cancer cases in Taiwan are similar albeit less conclusive with respect to comparing the approaches. The success of the spectral basis with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.

Procedimento para estudo da escolha modal em viagens realizadas por estudantes de instituições de ensino médio, mediado pela psicologia social

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2016.

A quasi-maximum likelihood method for estimating the parameters of multivariate diffusions

A quasi-maximum likelihood procedure for estimating the parameters of multi-dimensional diffusions is developed in which the transitional density is a multivariate Gaussian density with first and second moments approximating the true moments of the unknown density. For affine drift and diffusion functions, the moments are exactly those of the true transitional density and for nonlinear drift and diffusion functions the approximation is extremely good and is as effective as alternative methods based on likelihood approximations. The estimation procedure generalises to models with latent factors. A conditioning procedure is developed that allows parameter estimation in the absence of proxies.

Quasi steady state and dynamic hygrothermal performance of fibrous Hemp and Stone Wool insulations: Two innovative laboratory based investigations

Moisture and heat management properties of Hemp and Stone Wool insulations were studied by mounting them between a hot and a cold climate chamber. Both insulations were exposed to identical hygrothermal boundary conditions. Quasi steady state and dynamic tests were carried out at a range of relative humidity exposures. The likelihood of interstitial condensation was assessed and equivalent thermal conductivity values of the insulations were determined. The adsorption-desorption isotherms of the insulations were also determined in a dynamic vapour sorption (DVS) instrument. It was observed that the likelihood of condensation was higher in Stone Wool insulation than in Hemp insulation. Hemp insulation performed better in managing moisture due to its high hygric inertia and water absorption capacity. It was observed that the equivalent thermal conductivity of Stone Wool insulation was dependent on enthalpy flow and phase change of moisture. The equivalent thermal conductivity of Hemp insulation was close to its declared thermal conductivity in dynamic conditions when high relative humidity exposures were transient. In quasi steady state boundary conditions, when the insulation was allowed to reach the equilibrium moisture content at ranges of relative humidity, there was a moisture dependent increase of thermal conductivity in Hemp insulation.

Mental Health and Morbidity of Caregivers and Co-Residents of individuals with Dementia: A Quasi-Experimental Design

Objectives: To determine if providing informal care to a co-resident with dementia symptoms places an additional risk on the likelihood of poor mental health or mortality compared to co-resident non-caregivers.
Design: A quasi-experimental design of caregiving and non-caregiving co-residents of individuals with dementia symptoms, providing a natural comparator for the additive effects of caregiving on top of living with an individual with dementia symptoms. 
Methods: Census records, providing information on household structure, intensity of caregiving, presence of dementia symptoms and self-reported mental health, were linked to mortality records over the following 33 months. Multi-level regression models were constructed to determine the risk of poor mental health and death in co-resident caregivers of individuals with dementia symptoms compared to co-resident non-caregivers, adjusting for the clustering of individuals within households.
Results: The cohort consisted of 10,982 co-residents (55.1% caregivers), with 12.1% of non-caregivers reporting poor mental health compared to 8.4% of intense caregivers (>20 hours of care per week). During follow-up the cohort experienced 560 deaths (245 to caregivers). Overall, caregiving co-residents were at no greater risk of poor mental health but had lower mortality risk than non-caregiving co-residents (ORadj=0.93, 95% CI 0.79, 1.10 and ORadj=0.67, 95% CI 0.56, 0.81, respectively); this lower mortality risk was also seen amongst the most intensive caregivers (ORadj=0.65, 95% CI 0.53, 0.79).
Conclusion: Caregiving poses no additional risk to mental health over and above the risk associated with merely living with someone with dementia, and is associated with a lower mortality risk compared to non-caregiving co-residents.