Weibull inference using trimmed samples and prior information

Trimmed samples are commonly used in several branches of statistical methodology, especially when the presence of contaminated data is suspected. Assuming that certain proportions of the smallest and largest observations from a Weibull sample are unknown or have been eliminated, a Bayesian approach to point and interval estimation of the scale parameter, as well as hypothesis testing and prediction, is presented. In many cases, the use of substantial prior information can significantly increase the quality of the inferences and reduce the amount of testing required. Some Bayes estimators and predictors are derived in closed-forms. Highest posterior density estimators and credibility intervals can be computed using iterative methods. Bayes rules for testing one- and two-sided hypotheses are also provided. An illustrative numerical example is included.     

Does managerial ability influence the quality of financial reporting?

The purpose of this paper is to study the influence of managerial ability on the quality of their financial reporting. Using a large bank sample from nine different countries and for the time period 2004–2010, we expect that bank earnings quality and accounting conservatism increase with more able managers that disclose more accurate earnings and who report higher information about banks’ future earnings and cash flows.The results confirm that managerial abilities play a significant role in the quality of financial reporting in banks, and that capable bank managers are less likely to manage earnings opportunistically. This study is timely and relevant given the recent emphasis on earnings quality of banks over the last few years, and the criticisms of managerial abilities after the financial crisis. The evidence from this study can help standard-setters and regulators to better understand the business practices and accounting behavior of banks in the light of managerial abilities.     

Gerrymandering in a hierarchical legislature

Theory and Decision - We build a multiple hierarchical model of a representative democracy in which citizens elect ward representatives, ward representatives elect county representatives, county...     

Bayesian Analysis of Multiple Hypothesis Testing with Applications to Microarray Experiments

Recently, the field of multiple hypothesis testing has experienced a great expansion, basically because of the new methods developed in the field of genomics. These new methods allow scientists to simultaneously process thousands of hypothesis tests. The frequentist approach to this problem is made by using different testing error measures that allow to control the Type I error rate at a certain desired level. Alternatively, in this article, a Bayesian hierarchical model based on mixture distributions and an empirical Bayes approach are proposed in order to produce a list of rejected hypotheses that will be declared significant and interesting for a more detailed posterior analysis. In particular, we develop a straightforward implementation of a Gibbs sampling scheme where all the conditional posterior distributions are explicit. The results are compared with the frequentist False Discovery Rate (FDR) methodology. Simulation examples show that our model improves the FDR procedure in the sense that it diminishes the percentage of false negatives keeping an acceptable percentage of false positives.     

Predicting treatment efficacy via quantitative magnetic resonance imaging: a Bayesian joint model

The prognosis for patients with high grade gliomas is poor, with a median survival of 1 year. Treatment efficacy assessment is typically unavailable until 5-6 months post diagnosis. Investigators hypothesize that quantitative magnetic resonance imaging can assess treatment efficacy 3 weeks after therapy starts, thereby allowing salvage treatments to begin earlier. The purpose of this work is to build a predictive model of treatment efficacy by using quantitative magnetic resonance imaging data and to assess its performance. The outcome is 1-year survival status. We propose a joint, two-stage Bayesian model. In stage I, we smooth the image data with a multivariate spatiotemporal pairwise difference prior. We propose four summary statistics that are functionals of posterior parameters from the first-stage model. In stage II, these statistics enter a generalized non-linear model as predictors of survival status. We use the probit link and a multivariate adaptive regression spline basis. Gibbs sampling and reversible jump Markov chain Monte Carlo methods are applied iteratively between the two stages to estimate the posterior distribution. Through both simulation studies and model performance comparisons we find that we can achieve higher overall correct classification rates by accounting for the spatiotemporal correlation in the images and by allowing for a more complex and flexible decision boundary provided by the generalized non-linear model.     

Asymptotic behavior of RA-estimates in autoregressive 2D processes

In this work we study the asymptotic behavior of a robust class of estimators of the coefficient of a AR-2D process. We establish the precise conditions for the consistency and asymptotic normality of the RA estimator. The AR-2D model has many applications in image modeling and statistical image processing, therefore the relevance of knowing such properties. The adequacy of the AR-2D model is analyzed with real images; we also show the impact of contamination and the capability of the RA estimator to produce useful results even in the presence of spurious data.     

Toward two narratives of knowledge

This article states that the discipline of international relations has paid only scarce attention to the debates on knowledge that have taken place in neighboring disciplines. A notable exception is the epistemic community framework that amounts in this article to a first narrative of knowledge. By means of challenging this narrative of knowledge, the article suggests a second narrative of knowledge that is derived from literature in science and technology studies. This narrative is further illustrated through a discussion on the epistemic authority of the Forest Stewardship Council and the newly established Intergovernmental Platform on Biodiversity and Ecosystem Services.     

Relational Health and Masculine Gender-Role Conflict in the Friendships and Community Relationships of Bisexual,Gay, and Straight Men

This study examined how men's masculine gender-role conflict and the importance men placed on interpersonal relationships in defining their self-identities predicted their relational health experiences in same-sex, dyadic friendships and community relationships. Using an Internet sample of 283 self-identified bisexual, gay, and straight men, results of hierarchical linear regression analyses indicated that for men across sexual orientations, the more importance men placed on interpersonal relationships with other men, the greater the degree of relational health they experienced in dyadic friendships. Additionally, for bisexual and gay men in dyadic friendships with other men, gender-role conflict was inversely related to the relational health of their friendships. In the domain of community relationships, the importance that bisexual and straight men placed on interpersonal relationships in defining their self-identities and levels of gender-role conflict predicted relational health experiences. For gay men, however, feelings of masculine gender-role conflict, alone, predicted poorer relational health in community relationships. Study limitations, clinical implications for practice, and future research directions are discussed.