Bayesian Analysis of ANOVA and Mixed Models on the Log-Transformed Response Variable.

The analysis of variance, and mixed models in general, are popular tools for analyzing experimental data in psychology. Bayesian inference for these models is gaining popularity as it allows to easily handle complex experimental designs and data dependence structures. When working on the log of the response variable, the use of standard priors for the variance parameters can create inferential problems and namely the non-existence of posterior moments of parameters and predictive distributions in the original scale of the data. The use of the generalized inverse Gaussian distributions with a careful choice of the hyper-parameters is proposed as a general purpose option for priors on variance parameters. Theoretical and simulations results motivate the proposal. A software package that implements the analysis is also discussed. As the log-transformation of the response variable is often applied when modelling response times, an empirical data analysis in this field is reported.

Bayesian inference for quantiles of the log-normal distribution.

The log-normal distribution is very popular for modeling positive right-skewed data and represents a common distributional assumption in many environmental applications. Here we consider the estimation of quantiles of this distribution from a Bayesian perspective. We show that the prior on the variance of the log of the variable is relevant for the properties of the posterior distribution of quantiles. Popular choices for this prior, such as the inverse gamma, lead to posteriors without finite moments. We propose the generalized inverse Gaussian and show that a restriction on the choice of one of its parameters guarantees the existence of posterior moments up to a prespecified order. In small samples, a careful choice of the prior parameters leads to point and interval estimators of the quantiles with good frequentist properties, outperforming those currently suggested by the frequentist literature. Finally, two real examples from environmental monitoring and occupational health frameworks highlight the improvements of our methodology, especially in a small sample situation.

In vivo and in vitro effects of selected antioxidants on rabbit meat microbiota.

The purpose of this study was to investigate the effect of dietary vitamin E or EconomasE™ supplementation on the growth of several background/pathogenic bacteria on rabbit carcasses and hamburgers during refrigerated storage. For 51days, 270 New Zealand rabbits received either a basal diet, or experimental diets enriched with 100 or 200mg/kg of vitamin E or EconomasE™. The bacteria studied were Salmonella, Listeria monocytogenes, Pseudomonas, Enterobacteriaceae, Escherichia coli, coagulase-positive staphylococci, plus both mesophilic and psychrotrophic aerobes. The growth of Listeria monocytogenes on contaminated patties was evaluated through a challenge test. The potential protective or antimicrobial effect of vitamin E or EconomasE™ on Listeria monocytogenes or Pseudomonas aeruginosa was assessed in vitro. Diet did not influence the concentrations of bacteria found on rabbit carcasses and developing on hamburgers. Vitamin E (in vivo and in vitro) and EconomasE™ in vivo had a protective antioxidant role, while EconomasE™ in vitro had strong antibacterial activity against Listeria monocytogenes, but not against Pseudomonas aeruginosa.

A multivariate CAR model for improving the estimation of relative risks.

Disease mapping studies have been widely performed at univariate level, that is considering only one disease in the estimated models. Nonetheless, simultaneous modelling of different diseases can be a valuable tool both from the epidemiological and from the statistical point of view. In this paper we propose a model for multivariate disease mapping that generalizes the univariate conditional auto-regressive distribution. The proposed model is proven to be an effective alternative to existing multivariate models, mainly because it overcome some restrictive hypotheses underlying models previously proposed in this context. Model performances are checked via a simulation study and via application to a case study.

Biodiversity among Lactobacillus helveticus strains isolated from different natural whey starter cultures as revealed by classification trees.

Lactobacillus helveticus is a homofermentative thermophilic lactic acid bacterium used extensively for manufacturing Swiss type and aged Italian cheese. In this study, the phenotypic and genotypic diversity of strains isolated from different natural dairy starter cultures used for Grana Padano, Parmigiano Reggiano, and Provolone cheeses was investigated by a classification tree technique. A data set was used that consists of 119 L. helveticus strains, each of which was studied for its physiological characters, as well as surface protein profiles and hybridization with a species-specific DNA probe. The methodology employed in this work allowed the strains to be grouped into terminal nodes without difficult and subjective interpretation. In particular, good discrimination was obtained between L. helveticus strains isolated, respectively, from Grana Padano and from Provolone natural whey starter cultures. The method used in this work allowed identification of the main characteristics that permit discrimination of biotypes. In order to understand what kind of genes could code for phenotypes of technological relevance, evidence that specific DNA sequences are present only in particular biotypes may be of great interest.