Bivariate distributions with second kind beta conditionals

In this paper the most general bivariate distributions with second kind beta conditionals is fully characterized. This family is closed under inversions and the conditional moments are shown to be rational functions of the conditioned variable. Two subfamilies of dependent distributions is shown to have second kind beta marginals too. Finally, as a particular case, the most general bivariate distribution with second kind Pareto conditionals is characterized.     

Mixture inverse Gaussian distributions and its transformations,moments and applications

Skewed models are important and necessary when parametric analyses are carried out on data. Mixture distributions produce widely flexible models with good statistical and probabilistic properties, and the mixture inverse Gaussian (MIG) model is one of those. Transformations of the MIG model also create new parametric distributions, which are useful in diverse situations. The aim of this paper is to discuss several aspects of the MIG distribution useful for modelling positive data. We specifically discuss transformations, the derivation of moments, fitting of models, and a shape analysis of the transformations. Finally, real examples from engineering, environment, insurance, and toxicology are presented for illustrating some of the results developed here. Three of the four data sets, which have arisen from the consulting work of the authors, are new and have not been previously analysed. All these examples display that the empirical fit of the MIG distribution to the data is very good.     

Bayesian Methodology for Model Uncertainty Using Model Performance Data

A simple and useful characterization of many predictive models is in terms of model structure and model parameters. Accordingly, uncertainties in model predictions arise from uncertainties in the values assumed by the model parameters (parameter uncertainty) and the uncertainties and errors associated with the structure of the model (model uncertainty). When assessing uncertainty one is interested in identifying, at some level of confidence, the range of possible and then probable values of the unknown of interest. All sources of uncertainty and variability need to be considered. Although parameter uncertainty assessment has been extensively discussed in the literature, model uncertainty is a relatively new topic of discussion by the scientific community, despite being often the major contributor to the overall uncertainty. This article describes a Bayesian methodology for the assessment of model uncertainties, where models are treated as sources of information on the unknown of interest. The general framework is then specialized for the case where models provide point estimates about a single‐valued unknown, and where information about models are available in form of homogeneous and nonhomogeneous performance data (pairs of experimental observations and model predictions). Several example applications for physical models used in fire risk analysis are also provided.     

Clustering time series by linear dependency

Statistics and Computing - We present a new way to find clusters in large vectors of time series by using a measure of similarity between two time series, the generalized cross correlation. This...     

Sojourning With the Homogeneous Poisson Process

In this pedagogical article, distributional properties, some surprising, pertaining to the homogeneous Poisson process (HPP), when observed over a possibly random window, are presented. Properties of the gap-time that covered the termination time and the correlations among gap-times of the observed events are obtained. Inference procedures, such as estimation and model validation, based on event occurrence data over the observation window, are also presented. We envision that through the results in this article, a better appreciation of the subtleties involved in the modeling and analysis of recurrent events data will ensue, since the HPP is arguably one of the simplest among recurrent event models. In addition, the use of the theorem of total probability, Bayes’ theorem, the iterated rules of expectation, variance and covariance, and the renewal equation could be illustrative when teaching distribution theory, mathematical statistics, and stochastic processes at both the undergraduate and graduate levels. This article is targeted toward both instructors and students.     

A Methodology for Risk Analysis Based on Hybrid Bayesian Networks: Application to the Regasification System of Liquefied Natural Gas Onboard a Floating Storage and Regasification Unit

This article presents an iterative six‐step risk analysis methodology based on hybrid Bayesian networks (BNs). In typical risk analysis, systems are usually modeled as discrete and Boolean variables with constant failure rates via fault trees. Nevertheless, in many cases, it is not possible to perform an efficient analysis using only discrete and Boolean variables. The approach put forward by the proposed methodology makes use of BNs and incorporates recent developments that facilitate the use of continuous variables whose values may have any probability distributions. Thus, this approach makes the methodology particularly useful in cases where the available data for quantification of hazardous events probabilities are scarce or nonexistent, there is dependence among events, or when nonbinary events are involved. The methodology is applied to the risk analysis of a regasification system of liquefied natural gas (LNG) on board an FSRU (floating, storage, and regasification unit). LNG is becoming an important energy source option and the world's capacity to produce LNG is surging. Large reserves of natural gas exist worldwide, particularly in areas where the resources exceed the demand. Thus, this natural gas is liquefied for shipping and the storage and regasification process usually occurs at onshore plants. However, a new option for LNG storage and regasification has been proposed: the FSRU. As very few FSRUs have been put into operation, relevant failure data on FSRU systems are scarce. The results show the usefulness of the proposed methodology for cases where the risk analysis must be performed under considerable uncertainty.     

Over the Colombian Rainbow

This poststructural narrative research intends to contribute a better understanding of some lesbian, gay, bisexual, and transgendered (LGBT) people's meanings, interactions, and reflections through the analysis of Colombian government's master narratives and LGBT advocates’ counter-narratives. A better understanding between the Colombian government and LGBT activists is needed in order to build bridges that allow the parties to construct pathways to new stories that legitimize them and will support solutions to their conflicts. The author argues that LGBT counter-narratives are repairing the damaged identities of LGBT in Colombia, even though they have not become master narratives.     

The Role of Physical Context,Verbal Skills,Non‐parental Care,Social Support,and Type of Parental Discipline in the Development of ToM Capacity in Five‐Year‐Old Children

The aim of this study was to analyze the influence of family context on the performance of 5‐year‐old children (N = 70) in theory of mind (ToM) tasks. The children's performances were assessed during individual sessions held at school. Children's verbal skills were assessed using the Peabody picture vocabulary test (PPVT). Interviews were also conducted with parents in the family home in order to gather sociodemographic data and to assess diverse variables of the family context. In addition to confirming the important role played by children's verbal skills in ToM, the results also supported a potential influence of family context in ToM development through variables, such as quality of non‐parental care or level of social contact and support. The educational implications of these results are discussed in relation to family intervention.     

Overconfidence and risk seeking in credit markets: an experimental game

Behavioral biases may influence bank decisions when granting credit to their customers. This paper explores this possibility in an experimental setting, contributing to the literature in two ways. First, we designed a business simulation game that replicates the basic decision-making processes of a bank granting credit to clients under conditions of risk and uncertainty. Second, we implemented a series of short tests to measure participants’ overconfidence and risk profile according to prospect theory and then conduct an experimental implementation of the simulation game. We find that higher levels of overprecision and risk seeking for gains (mostly attributable to distortion of probabilities) foster lower prices and higher volumes of credit, and reduce quality. The most consistent result is that distortion of probabilities affects the ability to discriminate between the quality of borrowers according to objective information, fostering strategies of lower loan prices to lower quality clients. The external validity of the results is also discussed.