On a control chart for the Gini index with simulations

In this work we suggest the use of the Gini index on control charts. The asymptotic properties of Gini index are presented and the control charts based on appropriate confidence intervals are constructed. The suitability of the proposed charts are investigated by means of extensive simulations.     

In Search of Executive Impairment in Pathological Gambling: A Neuropsychological Study on Non-treatment Seeking Gamblers

Pathological gambling is characterized by a persisting maladaptive and recurrent behavior with severe social and psychological consequences. There is evidence of strong comorbidity with psychiatric manifestations as well as cognitive mainly involving executive functions. This study aimed to investigate impairment in executive functions and working memory, and personality traits in a sample of Greek gamblers. Twenty-four men involved in various gambling activities were recruited from ecological settings as probable pathological gamblers. They were assessed with a comprehensive neuropsychological battery involving several executive tasks, the Zuckerman–Kuhlman Personality Questionnaire, the Hospital Anxiety Depression Scale, and the Difficulties in Emotion Regulation Scale. An age- and education-level matched group of 21 men without history of habitual gambling served as controls. As a group, gamblers displayed significantly lower scores on indices of inhibition, decision making and self-reported emotional awareness, and scored higher on impulsivity/sensation seeking personality traits. Notably, gamblers scored similarly or significantly higher on measures of verbal and visuospatial working memory, cognitive flexibility, processing speed, verbal fluency, and sustained attention. Overall, we argue that gamblers do present with specific cognitive deficits, but there is no evidence for a generalized executive impairment, and further stress the importance of investigating cognitive, personality, and psychiatric aspects of gambling on the basis of an ecologically valid sampling.     

Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non-linear dependencies in stock returns

The main purpose of this work is to decompose the predictive performance of the moving average (MA) trading rule and find out the portion that could be attributed to the possible exploitation of linear and non-linear dependencies in stock returns. Data from the General Index of the Athens Stock Exchange, from the Standard and Poor-500 Index of the New York Stock Exchange and from the Austrian Traded Index of the Vienna Stock Exchange are filtered by linear filters so as the resulting simulated ‘returns’ exhibit no serial correlation. Applying MA trading rules to both the original and the simulated indices and using a new statistical testing procedure that takes into account the sensitivity of the performance of the trading rule as a function of the length of the MA it is found that the predictive performance of the trading rule is clearly weakened when applied to the simulated indices indicating that a substantial part of the rule's predictive performance is due to the exploitation of linear dependencies in stock returns. This weakening is uneven; in general the shorter the MA length the more pronounced the attenuation.     

A divergence test for autoregressive time series models

In this paper, we study the normality test for the innovations of unstable autoregressive models based on the divergence test. In order to investigate the asymptotic behavior of the tests, we use the link between the divergence test and the residual empirical process. Simulation results are provided for illustration.     

The use of GARCH models in VaR estimation

We evaluate the performance of an extensive family of ARCH models in modeling the daily Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of distributional assumptions and sample sizes. We find, first, that leptokurtic distributions are able to produce better one-step-ahead VaR forecasts; second, the choice of sample size is important for the accuracy of the forecast, whereas the specification of the conditional mean is indifferent. Finally, the ARCH structure producing the most accurate forecasts is different for every portfolio and specific to each equity index.     

On the advantages of the non-concave penalized likelihood model selection method with minimum prediction errors in large-scale medical studies

Variable and model selection problems are fundamental to high-dimensional statistical modeling in diverse fields of sciences. Especially in health studies, many potential factors are usually introduced to determine an outcome variable. This paper deals with the problem of high-dimensional statistical modeling through the analysis of the trauma annual data in Greece for 2005. The data set is divided into the experiment and control sets and consists of 6334 observations and 112 factors that include demographic, transport and intrahospital data used to detect possible risk factors of death. In our study, different model selection techniques are applied to the experiment set and the notion of deviance is used on the control set to assess the fit of the overall selected model. The statistical methods employed in this work were the non-concave penalized likelihood methods, smoothly clipped absolute deviation, least absolute shrinkage and selection operator, and Hard, the generalized linear logistic regression, and the best subset variable selection.The way of identifying the significant variables in large medical data sets along with the performance and the pros and cons of the various statistical techniques used are discussed. The performed analysis reveals the distinct advantages of the non-concave penalized likelihood methods over the traditional model selection techniques.     

Monte Carlo co-ordinate ascent variational inference

Statistics and Computing - In variational inference (VI), coordinate-ascent and gradient-based approaches are two major types of algorithms for approximating difficult-to-compute probability...     

A model selection criterion based on the BHHJ measure of divergence

The aim of this work is the discussion and investigation of measures of divergence and model selection criteria. A recently introduced measure of divergence, the so-called BHHJ measure (Basu, A., Harris, I.R., Hjort, N.L., Jones, M.C., 1998. Robust and efficient estimation by minimising a density power divergence. Biometrika 85, 549–559) is investigated and a new model selection criterion the divergence information criterion (DIC) based on this measure is proposed. Simulations are performed to check the appropriateness of the proposed criterion.     

Dis-embeddedness and de-classification: modernization politics and the Greek teacher unions in the 1990s

During the 90s, PASOK, in common with the other European social democratic parties has advocated Third Way revisionism and has placed the‘modernization’of the Greek society high on its political agenda. By focusing on a series of conflictual incidents between the teacher unions and the Greek government, this paper exemplifies the repercussion of this process on teachers‐state relations. Building upon the dialecties of path shaping and path dependency, we suggest that the particular development of the modernization project and the reactions it has triggered are to be attributed to the historically determined, nationally specific, contingently activated institutional legacies and structures that, on the one hand, stand in the way of the incorporation of Greece to the EMU/ESM and, on the other, are employed by the unions to defend industrial and social citizenship.