An EM algorithm for multivariate Poisson distribution and related models

Multivariate extensions of the Poisson distribution are plausible models for multivariate discrete data. The lack of estimation and inferential procedures reduces the applicability of such models. In this paper, an EM algorithm for Maximum Likelihood estimation of the parameters of the Multivariate Poisson distribution is described. The algorithm is based on the multivariate reduction technique that generates the Multivariate Poisson distribution. Illustrative examples are also provided. Extension to other models, generated via multivariate reduction, is discussed.     

Automatic Block-Length Selection for the Dependent Bootstrap

We review the different block bootstrap methods for time series, and present them in a unified framework. We then revisit a recent result of Lahiri [Lahiri, S. N. (1999b). Theoretical comparisons of block bootstrap methods, Ann. Statist. 27:386-404] comparing the different methods and give a corrected bound on their asymptotic relative efficiency; we also introduce a new notion of finite-sample “attainable” relative efficiency. Finally, based on the notion of spectral estimation via the flat-top lag-windows of Politis and Romano [Politis, D. N., Romano, J. P. (1995). Bias-corrected nonparametric spectral estimation. J. Time Series Anal. 16:67-103], we propose practically useful estimators of the optimal block size for the aforementioned block bootstrap methods. Our estimators are characterized by the fastest possible rate of convergence which is adaptive on the strength of the correlation of the time series as measured by the correlogram.     

Depressive Symptomatology in Greek Preadolescents: The Role of Parenting Style Perceptions

The relationships between preadolescents’ perceptions of their parents’ parenting style and depressive symptomatology were examined in a sample of 640 Greek preadolescents (mean age = 11.82 years, SD = .83). Children completed the Paternal Style and Dimensions Questionnaire, the Maternal Style and Dimensions Questionnaire, and the Children's Depression Inventory. According to the results, there is a significant link between parenting style (both for mothers and fathers) and depressive symptoms as manifested by preadolescents. In this study, gender differences were not found. Furthermore, the results indicate that preadolescents’ perception of their parents’ style is a significant factor in predicting depressive symptomatology. Finally, the present findings highlight the influence of parenting styles on children's psychological wellbeing.     

Straddling Two Continents: Social Policy and Welfare Politics in Turkey

There has been an increasing academic interest in understanding the dynamics of social policy in the Middle East and developing a conceptual ‘model’ to account for the particular characteristics of welfare arrangements in the countries of the region. While part of this framework, Turkey represents an exceptional case due to the Europeanization processes the country is undergoing in various policy areas, including social policy. The influence of the European Union on the shape of Turkish social policy, as illustrated by the government's recent reforms in the labour market and social security domains, is hereby used to outline the position of Turkey vis‐à‐vis both the Southern European welfare regime and the Middle Eastern pattern. This article seeks to assess the dynamics of Turkish social policy in light of the country's political, and socio‐economic dynamics, as well as the external influence exerted by the EU and international financial institutions. The aim is to examine Turkish welfare arrangements in a comparative manner and consider its suitability with reference to either of the two models. Looking at major trends in social security and the labour market, the article argues for a Turkish ‘hybrid’ model embodying the characteristics of both. Subject to EU explicit pressures for reform absent elsewhere in the Middle East, the data nevertheless show that Turkey has yet to make the qualitative leap forward that could place it firmly within the Southern European welfare group.     

Moment-based tail index estimation

A general method of tail index estimation for heavy-tailed time series, based on examining the growth rate of the logged sample second moment of the data was proposed and studied in Meerschaert and Scheffler (1998. A simple robust estimator for the thickness of heavy tails. J. Statist. Plann. Inference 71, 19–34) as well as Politis (2002. A new approach on estimation of the tail index. C. R. Acad. Sci. Paris, Ser. I 335, 279–282). To improve upon the basic estimator, we introduce a scale-invariant estimator that is computed over subsets of the whole data set. We show that the new estimator, under some stronger conditions on the data, has a polynomial rate of consistency for the tail index. Empirical studies explore how the new method compares with the Hill, Pickands, and DEdH estimators.     

Model-based clustering using copulas with applications

The majority of model-based clustering techniques is based on multivariate normal models and their variants. In this paper copulas are used for the construction of flexible families of models for clustering applications. The use of copulas in model-based clustering offers two direct advantages over current methods: (i) the appropriate choice of copulas provides the ability to obtain a range of exotic shapes for the clusters, and (ii) the explicit choice of marginal distributions for the clusters allows the modelling of multivariate data of various modes (either discrete or continuous) in a natural way. This paper introduces and studies the framework of copula-based finite mixture models for clustering applications. Estimation in the general case can be performed using standard EM, and, depending on the mode of the data, more efficient procedures are provided that can fully exploit the copula structure. The closure properties of the mixture models under marginalization are discussed, and for continuous, real-valued data parametric rotations in the sample space are introduced, with a parallel discussion on parameter identifiability depending on the choice of copulas for the components. The exposition of the methodology is accompanied and motivated by the analysis of real and artificial data.     

Investigating the role of orthogonal and non – orthogonal rotation in multivariate factor analysis,in regard to the repeatability of the extracted factors: A simulation study

ABSTRACT

Factor analysis (FA) is the most commonly used pattern recognition methodology in social and health research. A technique that may help to better retrieve true information from FA is the rotation of the information axes. The main goal is to test the reliability of the results derived through FA and to reveal the best rotation method under various scenarios. Based on the results of the simulations, it was observed that when applying non-orthogonal rotation, the results were more repeatable as compared to the orthogonal rotation, and, when no rotation was applied.