A class of dynamic piecewise exponential models with random time grid

A novel fully Bayesian approach for modeling survival data with explanatory variables using the Piecewise Exponential Model (PEM) with random time grid is proposed. We consider a class of correlated Gamma prior distributions for the failure rates. Such prior specification is obtained via the dynamic generalized modeling approach jointly with a random time grid for the PEM. A product distribution is considered for modeling the prior uncertainty about the random time grid, turning possible the use of the structure of the Product Partition Model (PPM) to handle the problem. A unifying notation for the construction of the likelihood function of the PEM, suitable for both static and dynamic modeling approaches, is considered. Procedures to evaluate the performance of the proposed model are provided. Two case studies are presented in order to exemplify the methodology. For comparison purposes, the data sets are also fitted using the dynamic model with fixed time grid established in the literature. The results show the superiority of the proposed model.     

Higher Moments and Prediction‐Based Estimation for the COGARCH(1,1) Model

COGARCH models are continuous time versions of the well‐known GARCH models of financial returns. The first aim of this paper is to show how the method of prediction‐based estimating functions can be applied to draw statistical inference from observations of a COGARCH(1,1) model if the higher‐order structure of the process is clarified. A second aim of the paper is to provide recursive expressions for the joint moments of any fixed order of the process. Asymptotic results are given, and a simulation study shows that the method of prediction‐based estimating function outperforms the other available estimation methods.     

Firth adjusted score function for monotone likelihood in the mixture cure fraction model

Lifetime Data Analysis - Models for situations where some individuals are long-term survivors, immune or non-susceptible to the event of interest, are extensively studied in biomedical research....     

Binge drinking vs. drunkenness. The questionable threshold of excess for young Italians

The use of a standard definition of ‘binge drinking’ can potentially offer the advantage of ‘objectifying’ the concept of excessive drinking. Nevertheless, the term has become somewhat confusing, as it is often used as a synonym of drunkenness, making cross-cultural comparison difficult. The present study investigates the meaning Italian young people attribute to binge drinking, to explain the gap between self-reported rates of drunkenness and episodes of binge drinking found by comparative youth drinking surveys. About 134 face-to-face semi-structured interviews were conducted, targeting adolescents (aged 15–17) and young adults (aged 22–24) who had admitted to drinking excessively. In addition, an online forum was created, using a video clip as a stimulus and asking for web users' comments (132 were analysed). Results show how in the view of Italian bingers, binge drinking does not necessarily entail drunkenness, but only being tipsy. This is what they aim at when they drink, while they have negative attitudes and expectations regarding intoxication and its effects. This boundary establishes the concept of excess and marks the threshold between socially acceptable and unacceptable drinking. In conclusion, the concept of binge drinking cannot be used as a synonym of drunkenness, which young people in Italy judge severely.     

Development and Governance in the Ex-Soviet Union: An Empirical Inquiry

The widespread adoption of the concept of wellbeing in the social sciences literature has allowed researchers to move beyond a narrow economic focus on income and consumption as the primary measure of inequality and poverty. Although statistical measurement and analyses of wellbeing have become increasingly feasible due to the availability of rich datasets, the empirical literature on wellbeing remains deficient in a number of ways. In this paper we argue that it is necessary that empirical studies of wellbeing encompass multiple. Applying Sen’s capability approach as our theoretical model we subsequently develop a modelling framework that applies the polychoric principal component analysis (PCA) method for the calculation of objective wellbeing and wellbeing inequality using household asset, education, health and housing dimensions. Findings from our case study of Vietnam challenge previous single dimensional analyses and reveal a number of layers to our understanding of wellbeing inequality in Vietnam. We argue that our use of polychoric PCA is especially suited to the analysis of wellbeing by explicitly incorporating the analysis of both cardinal and ordinal variables and overcoming deficiencies established in the literature relating to the use of standard PCA.

     

An Unbalance Adjustment Method for Development Indicators

This paper analyzes some aggregation aspects of the procedure for constructing a composite index on a multidimensional socio-economic phenomenon such as development, the main focus being on the unbalance among individual dimensions. First a theoretical framework is set up for the unbalance adjustment of the index. Then an aggregation function is proposed that takes unbalance among development dimensions into account; a separate index is also introduced that measures the unbalance itself. Finally the dataset of the Index of African Governance for the year 2007 is used to test this method and compare it against the weighted arithmetic mean of variables with relation to the measured values of unbalance, yielding significantly different results for ratings and rankings, which in addition show negative correlations with the unbalance adjustment values. The changes ensuing from the adjustment are commented for some countries.     

Concerns,Challenges, and Directions of Development for the Issue of Representing Uncertainty in Risk Assessment

In the analysis of the risk associated to rare events that may lead to catastrophic consequences with large uncertainty, it is questionable that the knowledge and information available for the analysis can be reflected properly by probabilities. Approaches other than purely probabilistic have been suggested, for example, using interval probabilities, possibilistic measures, or qualitative methods. In this article, we look into the problem and identify a number of issues that are foundational for its treatment. The foundational issues addressed reflect on the position that “probability is perfect” and take into open consideration the need for an extended framework for risk assessment that reflects the separation that practically exists between analyst and decisionmaker.     

Buying into the American Dream? Mexican Immigrants,Legal Status,and Homeownership in Los Angeles County*

Objectives. Contemporary patterns of homeownership reflect the continuing racial and ethnic stratification that exists in nearly all areas of American society. Of particular interest, especially within the context of recent immigration legislation, are the homeownership experiences of Mexican immigrants in the United States. Methods. The current study employs unique data from the 2001 Los Angeles County Mexican Immigrant Residency Status Survey (LAC‐MIRSS) to examine the association between diverse forms of legal status and homeownership for Mexican immigrants. Results. Analyses indicate that the relationship between legal status and housing tenure is not statistically significant, after accounting for economic, life‐course/life‐cycle, and assimilation/social capital characteristics. Conclusions. The lack of a significant relationship is contrary to past research, perhaps explained by the explosive growth of the subprime mortgage market in the United States; the increasing recognition by financial institutions of Latino immigrants as a largely untapped, yet emerging, market in the mortgage industry; the availability of alternative forms of identification; and the institutionalization of unauthorized immigration in Los Angeles.     

An Adaptive Simulation Framework for the Exploration of Extreme and Unexpected Events in Dynamic Engineered Systems

The end states reached by an engineered system during an accident scenario depend not only on the sequences of the events composing the scenario, but also on their timing and magnitudes. Including these additional features within an overarching framework can render the analysis infeasible in practical cases, due to the high dimension of the system state‐space and the computational effort correspondingly needed to explore the possible system evolutions in search of the interesting (and very rare) ones of failure. To tackle this hurdle, in this article we introduce a framework for efficiently probing the space of event sequences of a dynamic system by means of a guided Monte Carlo simulation. Such framework is semi‐automatic and allows embedding the analyst's prior knowledge about the system and his/her objectives of analysis. Specifically, the framework allows adaptively and intelligently allocating the simulation efforts preferably on those sequences leading to outcomes of interest for the objectives of the analysis, e.g., typically those that are more safety‐critical (and/or rare). The emerging diversification in the filling of the state‐space by the preference‐guided exploration allows also the retrieval of critical system features, which can be useful to analysts and designers for taking appropriate means of prevention and mitigation of dangerous and/or unexpected consequences. A dynamic system for gas transmission is considered as a case study to demonstrate the application of the method.     

Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas

We used simulation modeling to assess potential climate change impacts on wildfire exposure in Italy and Corsica (France). Weather data were obtained from a regional climate model for the period 1981–2070 using the IPCC A1B emissions scenario. Wildfire simulations were performed with the minimum travel time fire spread algorithm using predicted fuel moisture, wind speed, and wind direction to simulate expected changes in weather for three climatic periods (1981–2010, 2011–2040, and 2041–2070). Overall, the wildfire simulations showed very slight changes in flame length, while other outputs such as burn probability and fire size increased significantly in the second future period (2041–2070), especially in the southern portion of the study area. The projected changes fuel moisture could result in a lengthening of the fire season for the entire study area. This work represents the first application in Europe of a methodology based on high resolution (250 m) landscape wildfire modeling to assess potential impacts of climate changes on wildfire exposure at a national scale. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.