A finite mixture model for image segmentation

In this paper, we propose a model for image segmentation based on a finite mixture of Gaussian distributions. For each pixel of the image, prior probabilities of class memberships are specified through a Gibbs distribution, where association between labels of adjacent pixels is modeled by a class-specific term allowing for different interaction strengths across classes. We show how model parameters can be estimated in a maximum likelihood framework using Mean Field theory. Experimental performance on perturbed phantom and on real benchmark images shows that the proposed method performs well in a wide variety of empirical situations.     

Fiducial and Posterior Sampling

The fiducial coincides with the posterior in a group model equipped with the right Haar prior. This result is generalized here. For this the underlying probability space of Kolmogorov is replaced by a σ-finite measure space and fiducial theory is presented within this frame. Examples are presented that demonstrate that this also gives good alternatives to existing Bayesian sampling methods. It is proved that the results provided here for fiducial models imply that the theory of invariant measures for groups cannot be generalized directly to loops: there exist a smooth one-dimensional loop where an invariant measure does not exist.     

A finite mixture model for multivariate counts under endogenous selectivity

We describe a selection model for multivariate counts, where association between the primary outcomes and the endogenous selection source is modeled through outcome-specific latent effects which are assumed to be dependent across equations. Parametric specifications of this model already exist in the literature; in this paper, we show how model parameters can be estimated in a finite mixture context. This approach helps us to consider overdispersed counts, while allowing for multivariate association and endogeneity of the selection variable. In this context, attention is focused both on bias in estimated effects when exogeneity of selection (treatment) variable is assumed, as well as on consistent estimation of the association between the random effects in the primary and in the treatment effect models, when the latter is assumed endogeneous. The model behavior is investigated through a large scale simulation experiment. An empirical example on health care utilization data is provided.     

Generalized linear mixed joint model for longitudinal and survival outcomes

Longitudinal studies often entail categorical outcomes as primary responses. When dropout occurs, non-ignorability is frequently accounted for through shared parameter models (SPMs). In this context, several extensions from Gaussian to non-Gaussian longitudinal processes have been proposed. In this paper, we formulate an approach for non-Gaussian longitudinal outcomes in the framework of joint models. As an extension of SPMs, based on shared latent effects, we assume that the history of the response up to current time may have an influence on the risk of dropout. This history is represented by the current, expected, value of the response. Since the time a subject spends in the study is continuous, we parametrize the dropout process through a proportional hazard model. The resulting model is referred to as Generalized Linear Mixed Joint Model (GLMJM). To estimate model parameters, we adopt a maximum likelihood approach via the EM algorithm. In this context, the maximization of the observed data log-likelihood requires numerical integration over the random effect posterior distribution, which is usually not straightforward; under the assumption of Gaussian random effects, we compare Gauss-Hermite and Pseudo-Adaptive Gaussian quadrature rules. We investigate in a simulation study the behaviour of parameter estimates in the case of Poisson and Binomial longitudinal responses, and apply the GLMJM to a benchmark dataset.     

Rising powers,people rising: neo-liberalization and its discontents in the BRICS countries

ABSTRACT

The rise of the BRICS countries – Brazil, Russia, China, India, and South Africa – has called into question the future of Western dominance in world markets and geopolitics. However, the developmental trajectories of the BRICS countries are shot through with socio-economic fault lines that relegate large numbers of people to the margins of current growth processes, where life is characterized by multiple and overlapping vulnerabilities. These socio-economic fault lines have, in turn, given rise to political convulsions across the BRICS countries, ranging from single-issue protests to sustained social movements oriented towards structural transformation. This article presents an innovative theoretical framework for theorizing the emerging political economy of development in the BRICS countries centred on neo-liberalization, precarity, and popular struggles. It discusses the contributions to this special issue in terms of how they illuminate the intersection between neo-liberalization, precarity, and popular struggle in Brazil, Russia, India, China, and South Africa.     

Hybrid Governance: The Case of Household Solid Waste Management in Sweden

Governance is hybrid to the extent that it involves diverse organizational forms, jurisdictional domains, and stakeholders across the public and private sectors. This article analyses the governance of household solid waste management in Sweden from a hybridity perspective, with the City of Helsingborg in North-Western Scania in Sweden as an example. The governance arrangements comprise municipal and company-based organizations, conflicting legislation, and long planning and investment horizons. The latter has resulted in significant innovations over the last 20 years, while changes in organizational forms, tasks, networks and jurisdictions have caused tensions in governance processes and relationships.     

Modeling delay in diagnosis of NF: under reportincg,incidence and prevalence estimates

In this paper, we analyze data from the Italian National Register of Rare Diseases (NRRD) focusing, in particular, on the geo-temporal distribution of patients affected by neurofibromatosis type 1 (NF1, ICD9CM code 237.71). The aim is at deriving a corrected measure of incidence for the period 2007–2009 using a single source, and to provide NF1 prevalence estimates for the period 2001–2006 through the use of capture–recapture methods over two sources. In the first case, a reverse hazard estimator for the delay in diagnosis of NF1 is used to estimate the probability that a generic unit belonging to the population of interest has been registered by the archive of reference. For the second purpose, two-source capture–recapture methods have been used to estimate the number of NF1 prevalent units in Italy for the period 2001–2006, matching information provided by the NRRD and the national register of hospital discharge, Scheda di Dimissione Ospedaliera (in the following SDO), archives.