Comparison of Interval Estimators of Pr(X < Y) in the Two-parameter Exponential Distribution

We consider confidence intervals for the stress–strength reliability Pr(X< Y) in the two-parameter exponential distribution. We have derived the Bayesian highest posterior density interval using non-informative prior distributions. We have compared its performance with the intervals based on the generalized pivot variable intervals in terms of their coverage probabilities and expected lengths. Our simulation study shows that the Bayesian interval performs better according to the criteria used, especially when the sample sizes are very small. An example is given.     

Probability Generating Function Based Jeffrey's Divergence for Statistical Inference

Statistical inference procedures based on transforms such as characteristic function and probability generating function have been examined by many researchers because they are much simpler than probability density functions. Here, a probability generating function based Jeffrey's divergence measure is proposed for parameter estimation and goodness-of-fit test. Being a member of the M-estimators, the proposed estimator is consistent. Also, the proposed goodness-of-fit test has good statistical power. The proposed divergence measure shows improved performance over existing probability generating function based measures. Real data examples are given to illustrate the proposed parameter estimation method and goodness-of-fit test.     

Temporal Disaggregation: Methods,Information Loss,and Diagnostics

This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.     

Local influence diagnostics for incomplete overdispersed longitudinal counts

We develop local influence diagnostics to detect influential subjects when generalized linear mixed models are fitted to incomplete longitudinal overdispersed count data. The focus is on the influence stemming from the dropout model specification. In particular, the effect of small perturbations around an MAR specification are examined. The method is applied to data from a longitudinal clinical trial in epileptic patients. The effect on models allowing for overdispersion is contrasted with that on models that do not.     

Some maximum-indifference estimators for the slope of a univariate linear model

As known, the least-squares estimator of the slope of a univariate linear model sets to zero the covariance between the regression residuals and the values of the explanatory variable. To prevent the estimation process from being influenced by outliers, which can be theoretically modelled by a heavy-tailed distribution for the error term, one can substitute covariance with some robust measures of association, for example Kendall's tau in the popular Theil–Sen estimator. In a scarcely known Italian paper, Cifarelli [(1978), ‘La Stima del Coefficiente di Regressione Mediante l'Indice di Cograduazione di Gini’, Rivista di matematica per le scienze economiche e sociali, 1, 7–38. A translation into English is available at http://arxiv.org/abs/1411.4809 and will appear in Decisions in Economics and Finance] shows that a gain of efficiency can be obtained by using Gini's cograduation index instead of Kendall's tau. This paper introduces a new estimator, derived from another association measure recently proposed. Such a measure is strongly related to Gini's cograduation index, as they are both built to vanish in the general framework of indifference. The newly proposed estimator is shown to be unbiased and asymptotically normally distributed. Moreover, all considered estimators are compared via their asymptotic relative efficiency and a small simulation study. Finally, some indications about the performance of the considered estimators in the presence of contaminated normal data are provided.     

Vulnerabilities and risks in population and environment studies

In the study of risks , different sciences use the same category in different ways, each related to its own ontological assumptions. But many of these fields communicate very little with one another. This article seeks to approximate two of these areas of study that have shown similar concerns and that can mutually strengthen one another, namely, geography and demography. Geography was one of the first disciplines to include risk in its environmental dimension and has had broad experience in simultaneously focusing on social and natural dynamics. Demography, on the other hand, faces greater difficulties because only recently has it incorporated the environmental dimension into its scientific scope. Both have brought the concept of vulnerability into their conceptual framework as complementary to that of risk. Geographers understand vulnerability as a more symbiotic form of the relationship between society and nature, whereas demographers give it a strong socioeconomic component. In this regard, the conceptual discussion on risks and vulnerabilities, in its attempt at approximating these two fields, is a way of conceptually advancing and strengthening the different approaches to empirical work, especially in population–environment studies which is the common ground for the dialogue between the two disciplines.

Does the Euro–Mediterranean Partnership contribute to regional integration?

The paper investigates whether Southern Mediterranean and Middle Eastern markets under the Euro–Mediterranean Partnership (Egypt, Lebanon, Morocco, Malta, and Turkey) have become more financially integrated with the European stock market over time. The findings suggest that the Turkish equity market is moderately integrated with the European market, while the other economies exhibit weak financial integration with Europe, supporting the idea that the partnership appears to have no effect on enhancing inter-market linkages for these economies. Therefore, these markets would be good destinations for international investors seeking attractive investment opportunities to diversify their equity portfolios. Structural changes in the cross-market integration do exist, which may be considered as a guide for international equity portfolio diversification over different subperiods. In addition, there is evidence of an increasing trend in conditional correlations for Egypt and Turkey, to varying degrees, over time, notably during the post global financial crisis of 2007–2008, thus revealing herding behavior during this period. Overall, investors should be wary of the variation of equity market integration over time before engaging in an investment at the level of portfolio management and diversification. Policymakers must be aware of the remoteness of the current achievements of the Euro–Mediterranean Partnership from the targets set in terms of financial development and economic growth.