Multivariate log-skewed distributions with normal kernel and their applications

We introduce two classes of multivariate log-skewed distributions with normal kernel: the log canonical fundamental skew-normal (log-CFUSN) and the log unified skew-normal. We also discuss some properties of the log-CFUSN family of distributions. These new classes of log-skewed distributions include the log-normal and multivariate log-skew normal families as particular cases. We discuss some issues related to Bayesian inference in the log-CFUSN family of distributions, mainly we focus on how to model the prior uncertainty about the skewing parameter. Based on the stochastic representation of the log-CFUSN family, we propose a data augmentation strategy for sampling from the posterior distributions. This proposed family is used to analyse the US national monthly precipitation data. We conclude that a high-dimensional skewing function lead to a better model fit.     

Local mobilisations and the formation of environmental networks in a democratizing Tunisia

ABSTRACT

This study examines the environmental protests that occurred in Tunisia after the 2011 uprisings. It analyses the factors underpinning the rise of the environmental networks during the period of transition (2011–2014). It details the mobilising strategies that were crucial for the networks’ growth or survival during this period of institutional instability. The study shows how networks leaders were able to bring together social and political actors from different backgrounds and ideological orientations. It is argued that the ability of networks to develop new distinctive collective identities was crucial for network sustainability. Those networks and actors who did not develop new clearly defined environmental identities and continued to rely importantly on pre-existing (authoritarian) structures and practices were more negatively impacted by ideological cleavages and political calculations. Empirically, the contribution builds on interviews and observations, as well as documents collected from Tunisian municipalities between 2013 and 2015. Conceptually, the research proposes a bottom-up perspective that highlights the interplay between micro- and macro-dynamics and strategies during a political transition. The analysis details the actors’ capacity to build alliances via interpersonal relations at the micro level, and their strategies to engage with institutional actors and processes.     

Estimating the grid of time-points for the piecewise exponential model

One of the greatest challenges related to the use of piecewise exponential models (PEMs) is to find an adequate grid of time-points needed in its construction. In general, the number of intervals in such a grid and the position of their endpoints are ad-hoc choices. We extend previous works by introducing a full Bayesian approach for the piecewise exponential model in which the grid of time-points (and, consequently, the endpoints and the number of intervals) is random. We estimate the failure rates using the proposed procedure and compare the results with the non-parametric piecewise exponential estimates. Estimates for the survival function using the most probable partition are compared with the Kaplan-Meier estimators (KMEs). A sensitivity analysis for the proposed model is provided considering different prior specifications for the failure rates and for the grid. We also evaluate the effect of different percentage of censoring observations in the estimates. An application to a real data set is also provided. We notice that the posteriors are strongly influenced by prior specifications, mainly for the failure rates parameters. Thus, the priors must be fairly built, say, really disclosing the expert prior opinion.     

EM-Type algorithms for heavy-tailed logistic mixed models

This paper aims at evaluating different aspects of Monte Carlo expectation – maximization algorithm to estimate heavy-tailed mixed logistic regression (MLR) models. As a novelty it also proposes a multiple chain Gibbs sampler to generate of the latent variables distributions thus obtaining independent samples. In heavy-tailed MLR models, the analytical forms of the full conditional distributions for the random effects are unknown. Four different Metropolis–Hastings algorithms are assumed to generate from them. We also discuss stopping rules in order to obtain more efficient algorithms in heavy-tailed MLR models. The algorithms are compared through the analysis of simulated and Ascaris Suum data.     

Bayesian and Classical Approaches for Hypotheses Testing in Trisomies

We introduce classical approaches for testing hypotheses on the meiosis I non disjunction fraction in trisomies, such as the likelihood-ratio, bootstrap, and Monte Carlo procedures. To calculate the p-values for the bootstrap and Monte Carlo procedures, different transformations in the data are considered. Bootstrap confidence intervals are also used as a tool to perform hypotheses tests. A Monte Carlo study is carried out to compare the proposed test procedures with two Bayesian ones: Jeffreys and Pereira-Stern tests. The results show that the likelihood-ratio and the Bayesian tests present the best performance. Down syndrome data are analyzed to illustrate the procedures.     

Combinatorial analysis for route first-cluster second vehicle routing

Two Route first-cluster second vehicle routing algorithms are contrasted in the first section of the paper. Next, the ‘large’ number of feasible solutions to a multiple travelling salesman problem is established given that each salesman can visit any number of customers in a stated range. An approximate expression is given for the ‘small’ fraction of this solution space searched by a route first-cluster second vehicle routing heuristic. Nevertheless, this heuristic is seen to be a very efficient means of searching its solution space.     

Comparing products using data envelopment analysis

Using an application of linear programming known as Data Envelopment Analysis (DEA), a method is described to compare products which vary in excellence along a number of dimensions, and for each of which there might be a number of associated “costs”. The method is illustrated by comparing published benchmarks of 37 computer printers. Potential uses of a DEA analysis of products might be: to assist corporate buyers who may need to reconcile a diversity of present and future uses in one standardised purchase; in competitor analysis; in determining unexplored market niches; and as a normative model of product excellence against which product purchasing behaviour could be compared.