A Bayesian model for cross-study differential gene expression

In this paper we define a hierarchical Bayesian model for microarray expression data collected from several studies and use it to identify genes that show differential expression between two conditions. Key features include shrinkage across both genes and studies, and flexible modeling that allows for interactions between platforms and the estimated effect, as well as concordant and discordant differential expression across studies. We evaluated the performance of our model in a comprehensive fashion, using both artificial data, and a "split-study" validation approach that provides an agnostic assessment of the model's behavior not only under the null hypothesis, but also under a realistic alternative. The simulation results from the artificial data demonstrate the advantages of the Bayesian model. The 1 - AUC values for the Bayesian model are roughly half of the corresponding values for a direct combination of t- and SAM-statistics. Furthermore, the simulations provide guidelines for when the Bayesian model is most likely to be useful. Most noticeably, in small studies the Bayesian model generally outperforms other methods when evaluated by AUC, FDR, and MDR across a range of simulation parameters, and this difference diminishes for larger sample sizes in the individual studies. The split-study validation illustrates appropriate shrinkage of the Bayesian model in the absence of platform-, sample-, and annotation-differences that otherwise complicate experimental data analyses. Finally, we fit our model to four breast cancer studies employing different technologies (cDNA and Affymetrix) to estimate differential expression in estrogen receptor positive tumors versus negative ones. Software and data for reproducing our analysis are publicly available.     

Bayesian analysis of dynamic magnetic resonance breast images

Summary.  We describe an integrated methodology for analysing dynamic magnetic resonance images of the breast. The problems that motivate this methodology arise from a collaborative study with a tumour institute. The methods are developed within the Bayesian framework and comprise image restoration and classification steps. Two different approaches are proposed for the restoration. Bayesian inference is performed by means of Markov chain Monte Carlo algorithms. We make use of a Metropolis algorithm with a specially chosen proposal distribution that performs better than more commonly used proposals. The classification step is based on a few attribute images yielded by the restoration step that describe the essential features of the contrast agent variation over time. Procedures for hyperparameter estimation are provided, so making our method automatic. The results show the potential of the methodology to extract useful information from acquired dynamic magnetic resonance imaging data about tumour morphology and internal pathophysiological features.     

Inequality indices: theoretical and empirical aspects of their asymptotic behaviour

The authors use a Berry-Esseen type bound to identify the factors which influence the speed of convergence to the normal distribution of the indices of Gini, Piesch and Mehran. To empirically confirm the conclusions reached, a Monte Carlo experiment is performed for a log-logistic distribution.     

Evaluating industrial technological forecasting

This article discusses an approach to the application of technological forecasting among a group of Italian companies. The information upon which the article is based has been obtained from a preliminary study. However, it should prove of interest to manufacturing organizations where similar problems necessitate a constructive approach to forecasting.     

Algebraic statistics of level codings for fractional factorial designs

We discuss the applications of algebraic statistics to fractional factorial design with special emphasis on the choice of level coding. In particular, we deal with the theory of Bayley's level codings in that framework.     

Indicator function and complex coding for mixed fractional factorial designs

In a general fractional factorial design, the n levels of a factor are coded by the nth roots of the unity. This device allows a full generalization to mixed-level designs of the theory of the polynomial indicator function which has already been introduced for two-level designs in a joint paper with Fontana. The properties of orthogonal arrays and regular fractions are discussed.     

The Dimensions of Agricultural Diversification: A Spatial Analysis of Italian Municipalities

Diversification in agriculture could ensure the survival of farming by broadening the income base of farms and encouraging agricultural sustainability. The diversification of on-farm activities may depend on socioeconomic and environmental characteristics. Although multifunctionality is a hotly debated topic, few papers have focused on the farm characteristics that influence diversification and none have focused on the influence of the spatial pattern. This paper examines the relationship between farm characteristics and the adoption of a specific type of diversification. An econometric model is presented that uses a spatial autoregressive lag model at the municipal scale. The results demonstrate that the activation of each diversification dimension is influenced by both internal and external factors, encompassing farmers’ characteristics, the farm structure, and territorial features, including regional and spatial patterns. Deepening is mainly influenced by the farmer’s age, education, and the presence of small and labor-intensive farms. The key factors for broadening diversification are the presence of small farms and farms with mixed production (breeding and crop cultivation). Regrounding is most affected by the labor-intensive farms in the area and the farms with mixed production.     

The Determinants of Child Labour: The Role of Primary Product Specialization

Abstract. The paper tests predictions of a traditional intra‐household bargaining model which, under reasonable assumptions, shows that lack of bargaining power in the value chain significantly reduces the capacity for obtaining benefits from increased product demand arising from trade liberalization and therefore is positively associated with child labour. Cross‐sectional and panel negative binomial estimates in a sample of emerging countries support this hypothesis. They show that proxies of domestic workers’ bargaining power in the international division of labour (such as the share of primary product exports) are significantly related to child labour, net of the effect of traditional controls such as parental income, quality of education, international aid, and trade liberalization. The positive impact of the share of primary product exports on child labour outlines a potential paradox. The paradox suggests that trade liberalization does not always have straightforward positive effects on social indicators and that its short‐run effects on income distribution and distribution of skills and market power across countries need to be carefully evaluated.