A Generalized Log-Normal Model for Grouped Survival Data

It is common to have experiments in which it is not possible to observe the exact lifetimes but only the interval where they occur. This sort of data presents a high number of ties and it is called grouped or interval-censored survival data. Regression methods for grouped data are available in the statistical literature. The regression structure considers modeling the probability of a subject's survival past a visit time conditional on his survival at the previous visit. Two approaches are presented: assuming that lifetimes come from (1) a continuous proportional hazards model and (2) a logistic model. However, there may be situations in which none of the models are adequate for a particular data set. This article proposes the generalized log-normal model as an alternative model for discrete survival data. This model was introduced by Chen (1995 Chen , G. ( 1995 ). Generalized Log-normal distributions with reliability application . Comput. Stat. Data Anal. 19 : 300 – 319 . [Google Scholar]) and it is extended in this article for grouped survival data. A real example related to a Chagas disease illustrates the proposed model.     

Varying levels of anomie in Europe: a multilevel analysis based on multidimensional IRT models

Recent years have seen increased attention paid to monitoring social anomie and its dependency on micro- and macro-factors. In this paper, we endorse the theorisation of social anomie as a complex, multidimensional and multilevel phenomenon. To ensure a rigorous measurement of the varying levels of social anomie in the European countries, the current study relies on a multilevel multidimensional item response theory model which explicitly accounts for the presence of a non-ignorable missing data mechanism. This unified approach makes it possible to specify an analytical model of links between anomie features and their determinants and to explore how the latent traits of interest are influenced by individual-level factors, as well as by country-level indicators. Additionally, to avoid misleading inferential conclusions, the proposed model takes into account the respondent’s omitting behaviour, assuming that the missingness mechanism is driven by a latent propensity to respond. Data used in this study have been collected in the 2010 wave of the European Social Survey. To reduce the computational complexities, a Bayesian specification of the MIRT model is provided and the parameter model estimates are obtained through MCMC algorithms.     

Evidence for multiple strategies in choice under risk

This paper describes an experimental study that yields evidence for the coexistence of two decision strategies of choice under risk. Under the first strategy, choices are made based on aspiration levels – a heuristic that simplifies risky decisions. Under the second strategy, which can be used when aspiration levels are not determinative, choices are made based on preferences for positive skewness. Our model fitting confirms the efficacy of a two-pronged approach that can marshal either strategy depending on specific features of the risky prospects under consideration.     

Statistics and Politics in a “Knowledge Society”

The importance of information in economic and political processes is widely recognised by modern theories. This information, coupled with the advancements in Information and Communication Technologies (ICT) has changed the way in which markets and societies work. The availability of the Internet and other advanced forms of media have made information more accessible to citizens than ever before. Therefore, the ideal of the “fully informed decision maker” should be a reality. Unfortunately, this is far from the case. As Einstein put it, “information is not knowledge” and although citizens are bombarded by information on a constant basis, this bombardment does not necessarily bring about knowledge. Several studies have analysed the characteristics of the knowledge society, as well as its impact on the production of “official” statistics. In this paper we do not enter into this debate, but we analyse the role of statistics in building a knowledge society and improving the democratic control of policy makers. The paper analyses the relationships between information, expectations and economic theory, as well as the nexus between information and political sciences. Various approaches to the measurement of societal progress and the role of “key indicators” in this respect are also discussed. Finally, both theoretical models and empirical evidence about what citizens know on societal progress are discussed and the OECD project on the measurement of societal progress is presented.

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.     

A multiple imputation approach to deal with the unity measure error

Unity measure errors (UME) in numerical survey data can determine serious bias in the estimates of interest. In this paper, a finite Gaussian mixture model is used to identify observations affected by UME and to robustly estimate the target parameters in presence of this type of error. In the proposed model, the mixture components are associated to the different error patterns across the variables. We follow a multiple imputation approach in a Bayesian setting that allows us to handle missing values in data. In this framework, the assessment of the uncertainty associated with both errors and missingness is based on repeatedly drawing from the predictive distribution of the true non contaminated data given the observed data. The draws are obtained through a suitable version of the data augmentation algorithm. Applications to both simulated and real data are presented.     

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.