Estimating the efficiency of Brazilian electricity distribution utilities.

This paper proposes a differing methodology from the Brazilian Electricity Regulatory Agency on the efficiency estimation for the Brazilian electricity distribution sector. Our proposal combines robust state-space models and stochastic frontier analysis to measure the operational cost efficiency in a panel data set from 60 Brazilian electricity distribution utilities. The modeling joins the main literature in energy economics with advanced econometric and statistic techniques in order to estimate the efficiencies. Moreover, the suggested model is able to deal with changes in the inefficiencies across time whilst the Bayesian paradigm - through Markov chain Monte Carlo techniques - facilitates the inference on all unknowns. The method enables a significant degree of flexibility in the resultant efficiencies and a complete photography about the distribution sector.

Modelling diagnostics for Echinococcus granulosus surveillance in sheep using Latent Class Analysis: Argentina as a case study.

Echinococcus granulosus sensu lato is a globally prevalent zoonotic parasitic cestode leading to cystic echinococcosis (CE) in both humans and sheep with both medical and financial impacts, whose reduction requires the application of a One Health approach to its control. Regarding the animal health component of this approach, lack of accurate and practical diagnostics in livestock impedes the assessment of disease burden and the implementation and evaluation of control strategies. We use of a Bayesian Latent Class Analysis (LCA) model to estimate ovine CE prevalence in sheep samples from the Río Negro province of Argentina accounting for uncertainty in the diagnostics. We use model outputs to evaluate the performance of a novel recombinant B8/2 antigen B subunit (rEgAgB8/2) indirect enzyme-linked immunosorbent assay (ELISA) for detecting E. granulosus in sheep. Necropsy (as a partial gold standard), western blot (WB) and ELISA diagnostic data were collected from 79 sheep within two Río Negro slaughterhouses, and used to estimate individual infection status (assigned as a latent variable within the model). Using the model outputs, the performance of the novel ELISA at both individual and flock levels was evaluated, respectively, using a receiver operating characteristic (ROC) curve, and simulating a range of sample sizes and prevalence levels within hypothetical flocks. The estimated (mean) prevalence of ovine CE was 27.5% (95%Bayesian credible interval (95%BCI): 13.8%-58.9%) within the sample population. At the individual level, the ELISA had a mean sensitivity and specificity of 55% (95%BCI: 46%-68%) and 68% (95%BCI: 63%-92%), respectively, at an optimal optical density (OD) threshold of 0.378. At the flock level, the ELISA had an 80% probability of correctly classifying infection at an optimal cut-off threshold of 0.496. These results suggest that the novel ELISA could play a useful role as a flock-level diagnostic for CE surveillance in the region, supplementing surveillance activities in the human population and thus strengthening a One Health approach. Importantly, selection of ELISA cut-off threshold values must be tailored according to the epidemiological situation.

Study of the effect of genotype-environment interaction on age at first calving and production traits in Nellore cattle using multi-trait reaction norms and Bayesian inference.

This study investigated the effects of genotype-environment interaction on yearling weight, age at first calving and post-weaning weight gain in Nellore cattle using multi-trait reaction norm models. The environmental gradient was defined as a function of the mean yearling weight of the contemporary groups. A first-order random regression sire model with four classes of residual variance was used in the analyses and Bayesian methods were applied to estimate the (co)variance components. The heritability estimates ranged from 0.284 to 0.547, 0.222 to 0.316 and 0.256 to 0.522 for yearling weight, age at first calving and post-weaning weight gain, respectively. The lowest genetic correlations between environment groups for each trait were 0.38, 0.02 and 0.04 for yearling weight, age at first calving and post-weaning weight gain, respectively. Differences in the correlation estimates were observed between traits in the same environments, with the magnitude of the estimates tending toward zero as the environment improved. The results highlight the importance of including genotype-environment interactions in genetic evaluation programs considering the differences observed between environmental groups not only in terms of heritability, but also of genetic correlations.

Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction.

Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a large sample size ([Formula: see text]) and a small number of parameters (p) cannot be used for genomic-enabled prediction where the number of parameters (p) is larger than the sample size ([Formula: see text]). Here, we propose a Bayesian mixed-negative binomial (BMNB) genomic regression model for counts that takes into account genotype by environment [Formula: see text] interaction. We also provide all the full conditional distributions to implement a Gibbs sampler. We evaluated the proposed model using a simulated data set, and a real wheat data set from the International Maize and Wheat Improvement Center (CIMMYT) and collaborators. Results indicate that our BMNB model provides a viable option for analyzing count data.

Bayesian path specific frailty models for multi-state survival data with applications.

Multi-state models can be viewed as generalizations of both the standard and competing risks models for survival data. Models for multi-state data have been the theme of many recent published works. Motivated by bone marrow transplant data, we propose a Bayesian model using the gap times between two successive events in a path of events experienced by a subject. Path specific frailties are introduced to capture the dependence structure of the gap times in the paths with two or more states. Under improper prior distributions for the parameters, we establish propriety of the posterior distribution. An efficient Gibbs sampling algorithm is developed for drawing samples from the posterior distribution. An extensive simulation study is carried out to examine the empirical performance of the proposed approach. A bone marrow transplant data set is analyzed in detail to further demonstrate the proposed methodology.

Bayesian analysis for nonlinear mixed-effects models under heavy-tailed distributions.

A common assumption in nonlinear mixed-effects models is the normality of both random effects and within-subject errors. However, such assumptions make inferences vulnerable to the presence of outliers. More flexible distributions are therefore necessary for modeling both sources of variability in this class of models. In the present paper, I consider an extension of the nonlinear mixed-effects models in which random effects and within-subject errors are assumed to be distributed according to a rich class of parametric models that are often used for robust inference. The class of distributions I consider is the scale mixture of multivariate normal distributions that consist of a wide range of symmetric and continuous distributions. This class includes heavy-tailed multivariate distributions, such as the Student's t and slash and contaminated normal. With the scale mixture of multivariate normal distributions, robustification is achieved from the tail behavior of the different distributions. A Bayesian framework is adopted, and MCMC is used to carry out posterior analysis. Model comparison using different criteria was considered. The procedures are illustrated using a real dataset from a pharmacokinetic study. I contrast results from the normal and robust models and show how the implementation can be used to detect outliers.

Análise bayesiana do modelo de herança monogênica no melhoramento vegetal: um exemplo com abobrinha / Bayesian analisys of monogenic inheritance model in plant breeding: a case study with zucchini

Uma estratégia comum em programas de melhoramento é conduzir estudos básicos de herança para investigar a hipótese de controle do caráter por um ou poucos genes de efeito principal, associados ou não a genes modificadores de pequeno efeito. Neste trabalho, foi utilizada a inferência bayesiana para ajustar modelos de herança genética aditiva-dominante a experimentos de genética vegetal com várias gerações. Densidades normais com médias associadas aos efeitos genéticos das gerações foram consideradas em um modelo linear em que a matriz de delineamento dos efeitos genéticos tinha coeficientes indeterminados (precisando ser estimada para cada indivíduo). A metodologia foi ilustrada com um conjunto de dados de um estudo de herança da partenocarpia em abobrinha (Cucurbita pepo L). Tal ajuste permitiu explicitar a distribuição a posteriori das probabilidades genotípicas. A análise corrobora resultados anteriores da literatura, porém foi mais eficiente que alternativas prévias que supunham a matriz de delineamento conhecida para as gerações. Conclui-se que a partenocarpia em abobrinha é governada por um gene principal com dominância parcial.

A common breeding strategy is to carry out basic studies to investigate the hypothesis of a single gene controlling the trait (major gene) with or without polygenes of minor effect. In this study we used Bayesian inference to fit genetic additive-dominance models of inheritance to plant breeding experiments with multiple generations. Normal densities with different means, according to the major gene genotype, were considered in a linear model in which the design matrix of the genetic effects had unknown coefficients (which were estimated in individual basis). An actual data set from an inheritance study of partenocarpy in zucchini (Cucurbita pepo L.) was used for illustration. Model fitting included posterior probabilities for all individual genotypes. Analysis agrees with results in the literature but this approach was far more efficient than previous alternatives assuming that design matrix was known for the generations. Partenocarpy in zucchini is controlled by a major gene with important additive effect and partial dominance.

Inferência Bayesiana na análise genética de populações diplóides: estimação do coeficiente de endogamia e da taxa de fecundação cruzada / Bayesian inference in genetic analysis of diploid populations: inbreeding coefficient and outcrossing rate estimation

Neste estudo, utilizou-se a metodologia Bayesiana para estimar o coeficiente de endogamia e a taxa de fecundação cruzada de uma população diplóide por meio do modelo aleatório de COCKERHAM para freqüências alélicas. Um sistema de simulação de dados foi estruturado para validar a metodologia utilizada. O algoritmo Gibbs Sampler foi implementado no software R para obter amostras das distribuições marginais a posteriori para o coeficiente de endogamia e para a taxa de fecundação. O método Bayesiano mostrou-se eficiente na estimação dos parâmetros, pois os valores paramétricos utilizados na simulação encontravam-se dentro do intervalo de credibilidade de 95 por cento em todos os cenários considerados. A convergência do algoritmo Gibbs Sampler foi verificada, validando assim os resultados obtidos.

The Bayesian methodology was used to estimate the inbreeding coefficient and outcrossing rate in diploid populations by COCKERHAM random model to allelic frequency. The proposed methodology was evaluated by data simulation. The Gibbs Sampler algorithm was implemented in the R statistical software to obtain the random samples of the inbreeding coefficient and outcrossing rate posteriors marginal distributions. The Bayesian method showed good results, because the 95 percent credible intervals contained the true parameter values to all of the selected scenes. The Gibbs Sampler convergence was checked and this validated the estimation results.

Inferência Bayesiana na análise genética de populações diplóides: estimação do coeficiente de endogamia e da taxa de fecundação cruzada / Bayesian inference in genetic analysis of diploid populations: inbreeding coefficient and outcrossing rate estimation

Neste estudo, utilizou-se a metodologia Bayesiana para estimar o coeficiente de endogamia e a taxa de fecundação cruzada de uma população diplóide por meio do modelo aleatório de COCKERHAM para freqüências alélicas. Um sistema de simulação de dados foi estruturado para validar a metodologia utilizada. O algoritmo Gibbs Sampler foi implementado no software R para obter amostras das distribuições marginais a posteriori para o coeficiente de endogamia e para a taxa de fecundação. O método Bayesiano mostrou-se eficiente na estimação dos parâmetros, pois os valores paramétricos utilizados na simulação encontravam-se dentro do intervalo de credibilidade de 95 por cento em todos os cenários considerados. A convergência do algoritmo Gibbs Sampler foi verificada, validando assim os resultados obtidos.(AU)

The Bayesian methodology was used to estimate the inbreeding coefficient and outcrossing rate in diploid populations by COCKERHAM random model to allelic frequency. The proposed methodology was evaluated by data simulation. The Gibbs Sampler algorithm was implemented in the R statistical software to obtain the random samples of the inbreeding coefficient and outcrossing rate posteriors marginal distributions. The Bayesian method showed good results, because the 95 percent credible intervals contained the true parameter values to all of the selected scenes. The Gibbs Sampler convergence was checked and this validated the estimation results.(AU)