Bayesian analysis of censored linear regression models with scale mixtures of normal distributions

As is the case of many studies, the data collected are limited and an exact value is recorded only if it falls within an interval range. Hence, the responses can be either left, interval or right censored. Linear (and nonlinear) regression models are routinely used to analyze these types of data and are based on normality assumptions for the errors terms. However, those analyzes might not provide robust inference when the normality assumptions are questionable. In this article, we develop a Bayesian framework for censored linear regression models by replacing the Gaussian assumptions for the random errors with scale mixtures of normal (SMN) distributions. The SMN is an attractive class of symmetric heavy-tailed densities that includes the normal, Student-t, Pearson type VII, slash and the contaminated normal distributions, as special cases. Using a Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is introduced to carry out posterior inference. A new hierarchical prior distribution is suggested for the degrees of freedom parameter in the Student-t distribution. The likelihood function is utilized to compute not only some Bayesian model selection measures but also to develop Bayesian case-deletion influence diagnostics based on the q-divergence measure. The proposed Bayesian methods are implemented in the R package BayesCR. The newly developed procedures are illustrated with applications using real and simulated data.     

Conditional inference under simultaneous stochastic ordering constraints

Testing for stochastic ordering is of considerable importance when increasing does of a treatment are being compared, but in applications involving multivariate responses has received much less attention. We propose a permutation test for testing against multivariate stochastic ordering. This test is distribution-free and no assumption is made about the dependence relations among variables. A comparative simulation study shows that the proposed solution exhibits a good overall performance when compared with existing tests that can be used for the same problem.     

Ecological Economic Policy for Sustainable Development: Potentials and Domains of Intervention for Delinking Approaches

Due to the increasing environmental problems, conventional environmental policy will not suffice to secure a development path that can be sustainable on a global scale. This article establishes a conceptual framework for general strategies to reach the goals of ecological sustainability and individual well-being. Environmental impact, material input, income/production, the amount of services utilised, and well-being are the fundamental elements of this framework and their linkages are highlighted as possible targets of ecological economic policy. It is clear that current environmental policies, based on a rather narrow, reductionist view of the man-nature relationship, will not suffice. We investigate under which conditions a de-linking of individual well-being from environmental impacts can be achieved; a dramatic dematerialisation of the industrialised economies turns out to be a crucial element. This dematerialisation, we argue, can be achieved only putting a limit to quantitative economic growth, but nevertheless without decreasing the individual well-being, by concentrating the attention on highly valuable eco-efficient services rather than on the production/acquisition of material goods.     

On some aspects of decision theory under uncertainty: rationality,price-probabilities and the Dutch book argument

Theory and Decision - Choice under uncertainty is treated in economics by different approaches. We can distinguish three of them, two of which concern individual choice, while the third frames...