A Bayesian approach to zero-inflated data in extremes

Abstract

The generalized extreme value (GEV) distribution is known as the limiting result for the modeling of maxima blocks of size n, which is used in the modeling of extreme events. However, it is possible for the data to present an excessive number of zeros when dealing with extreme data, making it difficult to analyze and estimate these events by using the usual GEV distribution. The Zero-Inflated Distribution (ZID) is widely known in literature for modeling data with inflated zeros, where the inflator parameter w is inserted. The present work aims to create a new approach to analyze zero-inflated extreme values, that will be applied in data of monthly maximum precipitation, that can occur during months where there was no precipitation, being these computed as zero. An inference was made on the Bayesian paradigm, and the parameter estimation was made by numerical approximations of the posterior distribution using Markov Chain Monte Carlo (MCMC) methods. Time series of some cities in the northeastern region of Brazil were analyzed, some of them with predominance of non-rainy months. The results of these applications showed the need to use this approach to obtain more accurate and with better adjustment measures results when compared to the standard distribution of extreme value analysis.     

Categorías de género: consecuencias cognitivas sobre la identidad

Resumen

En este artículo se presentan algunas reflexiones en torno a los aspectos cognitivos de la identidad de género, ilustrándolas con datos procedentes de investigaciones realizadas en nuestro país. En primer lugar se aborda la cuestión de la importancia que la pertenencia a las categorías de género tiene para hombres y mujeres, así como la conciencia de esta pertenencia. Posteriormente se consideran las auto-percepciones que hombres y mujeres desarrollan como consecuencia de sus pertenencias categoriales de género, particularmente a la luz de los índices de masculinidad, feminidad y androginia encontrados en muestras españolas. Por último, se revisan algunos factores que influyen en la autopercepción en términos de las categorías de género: grado de identificación con el endogrupo de género, status de la categoría, características de la situación y tono evaluativo de los atributos estereotípicos.     

Latinos’ Changing Ethnic Group Representation From Elementary to Middle School: Perceived Belonging and Academic Achievement

This study examined the association between change in ethnic group representation from elementary to middle school and Latino students’ school belonging and achievement. The ethnic diversity of students’ middle school was examined as a moderator. Participants were 1,825 Latino sixth graders from 26 ethnically diverse urban middle schools. Hierarchical regression analyses showed that a change in ethnic representation toward fewer Latinos in middle school than elementary school was related to less perceived belonging and lower achievement in schools with low ethnic diversity. There were no mean differences as a function of declining representation in more diverse middle schools, suggesting that greater school diversity was protective. Findings highlight the importance of examining school ethnic context, especially across the middle school transition.     

Work situation operative model MOST: linking diagnosis and intervention to improve work conditions

This paper presents the model "Work Situation Operative Model" - MOST (after its Spanish acronym). It offers a comprehensive, systemic approach to analysing work stations and/or work processes, serving also as a framework for pursuing various ergonomic and occupational health and safety goals. Originally produced for a food sector company, the model has been extended and successfully applied in several industries in Colombia and Ecuador, including cement, oil, and paper industries. Based on a systemic understanding of work systems and tasks, the model not only allows different, commonly-used methods and tools for evaluating or assessing the risk of muscular-sketetal disorders to be included, but also supports occupational risk management strategies. Hence, one of its more important contributions relies on providing meaningful information that is useful for improving the work station and/or work process through design and re-design, by focusing on the interactions between all system elements.     

Asymptotic behaviour and statistical applications of divergence measures in multinomial populations: a unified study

Divergence measures play an important role in statistical theory, especially in large sample theories of estimation and testing. The underlying reason is that they are indices of statistical distance between probability distributions P and Q; the smaller these indices are the harder it is to discriminate between P and Q. Many divergence measures have been proposed since the publication of the paper of Kullback and Leibler (1951). Renyi (1961) gave the first generalization of Kullback-Leibler divergence, Jeffreys (1946) defined the J-divergences, Burbea and Rao (1982) introduced the R-divergences, Sharma and Mittal (1977) the (r,s)-divergences, Csiszar (1967) the ϕ-divergences, Taneja (1989) the generalized J-divergences and the generalized R-divergences and so on. In order to do a unified study of their statistical properties, here we propose a generalized divergence, called (h,ϕ)-divergence, which include as particular cases the above mentioned divergence measures. Under different assumptions, it is shown that the asymptotic distributions of the (h,ϕ)-divergence statistics are either normal or chi square. The chi square and the likelihood ratio test statistics are particular cases of the (h,ϕ)-divergence test statistics considered. From the previous results, asymptotic distributions of entropy statistics are derived too. Applications to testing statistical hypothesis in multinomial populations are given. The Pitman and Bahadur efficiencies of tests of goodness of fit and independence based on these statistics are obtained. To finish, apendices with the asymptotic variances of many well known divergence and entropy statistics are presented. The research in this paper was supported in part by DGICYT Grants N. PB91-0387 and N. PB91-0155. Their financial support is gratefully acknowledged.     

A weighted composite dose-response model for human salmonellosis.

This article describes the development of a weighted composite dose-response model for human salmonellosis. Data from previously reported human challenge studies were categorized into two different groups representing low and moderately virulent/pathogenic Salmonella strains based on a disease end point. Because epidemiological data indicate that some Salmonella strains are particularly pathogenic, and in the absence of human feeding study data for such strains, Shigella dysenteriae was used as a proxy for highly virulent strains. Three single-hit dose-response models were applied to the human feeding study data and evaluated for best fit using maximum likelihood estimation: (1) the exponential (E-1pop), (2) the two-subpopulation exponential (E-2pop), and (3) the Beta-Poisson (BP). Based on the goodness-of-fit test, the E-1pop and BP were the best-fit models for low and moderately virulent/pathogenic Salmonella strains, and the E-2pop and BP models were better for highly virulent/pathogenic strains. Epistemic analysis was conducted by determining the degree of confidence associated with the selected models, which was found to be 50%/50% (E-1pop/BP) for low and moderately pathogenic Salmonella strains, and 9.8%/90.2% (E-2pop/BP) for highly virulent strains. The degree of confidence for each component model and variations in the proportion of strains within each virulence/pathogenicity category were incorporated into the overall composite model. This study describes the influence of variation in strain virulence and host susceptibility on the shape of the population dose-response relationship.     

Strengths and coping strategies in the life narratives of sexual minority women

This study explored self-described strengths and strategies for coping with stress among sexual minority women (SMW), drawing on qualitative narratives of sexual minority and heterosexual women who were recruited from a population-based sample. In-depth follow-up qualitative telephone interviews were conducted with 48 women who had participated in the National Alcohol Survey, a U.S. population-based survey. Participants included 25 SMW and 16 matched exclusively heterosexual women. Narrative data were analyzed using inductive thematic analysis and constant comparison to explore the study aim, with an emphasis on themes that diverged or that were particularly salient for SMW relative to heterosexual women. Strengths and coping strategies that were especially meaningful in the narratives of sexual minority women emerged in two areas. First, participants described development of intrapersonal strengths through nurturing an authentic sense of self and embracing multifaceted identity. Second, participants described multiple strategies for cultivation of interpersonal resources: navigating distance and closeness with family of origin, cultivating supportive friends and chosen family, connecting to community, finding solace and joy with animals, and engaging in collective action. Findings underscore the importance of considering protective factors that are salient to SMW in developing or refining prevention and intervention efforts.     

A modified nested-error regression model for small area estimation

A nested-error regression model having both fixed and random effects is introduced to estimate linear parameters of small areas. The model is applicable to data having a proportion of domains where the variable of interest cannot be described by a standard linear mixed model. Algorithms and formulas to fit the model, to calculate EBLUP and to estimate mean-squared errors are given. A Monte Carlo simulation experiment is presented to illustrate the gain of precision obtained by using the proposed model and to obtain some practical conclusions. A motivating application to Spanish Labour Force Survey data is also given.     

Multiple stories: Collaborative and generative possibilities for psychological evaluation

This paper explores a collaborative generative approach to psychological assessment employed in Puerto Rico, with children, adolescents, and adults. It begins with a critical reflection on how the dominant discourse of mental health, founded in the suppositions and practices of Euro-American-centred psychological knowledge, has been disseminated to the globalise south under the claim that its assertions are unaffected by social, ideological, or historical forces. It discusses how its claims of expert scientific knowledge have contributed, through classificatory instruments such as the DSM and the ICD, to the production and re-production of deficit narratives in our day-to-day life. It also examines how these practices have been applied in the use of assessment instruments in Puerto Rico. It then describes, how, through a collaborative approach and narrative theory, the established colonising practices and narratives of traditional forms of psychological assessment can be questioned, deconstructed, and transformed. This approach promotes the co-creation of dialogic and generative spaces that allow for the emergence of multiple stories and performances that give meaning to a person's identity and relational being. A brief clinical case exposition is used to illustrate how this collaborative, dialogic, and culturally sensitive approach to psychological assessment can help to undermine and disrupt deficit-based narratives and provide families with new generative possibilities for re-storying and re-performing their lives and particularly, the lives of their children.