On statistical models for multimodal optimization

A new approach for the construction of statistical models for multimodal optimization is proposed; the examples of such models are given. The results of the psychological experiment show that the proposed approach is intuitively acceptable.     

Religiosity as a predictor of students' political attitudes

This paper analyzes the relationship between students' level of actual religiosity and their sociodemographic characteristics, political orientation, and political attitudes. The paper is based on an online survey conducted at the University of Rijeka (N = 624) in 2021. The vast majority of our respondents (90%) received the three holy sacraments of initiation in their early childhood and attended Catholic religious education at school. The explanation of the significantly less actual religiosity of students is approached from the position of Pickel's contextualized theory of secularization. Relying on Voas and Day, a composite variable distinguishes highly religious students, moderately religious students, weakly religious students, and non-religious students on the basis of the respondents' positioning toward religious self-identification, beliefs, Church attendance, and the importance of religion in one's life. Statistically significant correlations between actual religiosity and political orientation were established. Very religious Catholic students, who are the least numerous and the least homogenous category, are more inclined to right-wing political orientation and reject ethno-nationalism/anti-multiculturalism less and accept clericalism more than other categories of students. In a broader sense, this study reveals that a large number of respondents distance themselves from religion and the Church at student age despite their experience of formal religious socialization at school age. Furthermore, the results suggest that the synergy of the liberalizing effect of education and the tolerant sociocultural atmosphere of an area reduces the influence of religiosity on the political attitudes and orientation of students.     

Modeling and forecasting persistent financial durations

This article introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility model of Calvet and Fisher (2004) to the duration setting. Although the MSMD process is exponential β-mixing as we show in the article, it is capable of generating highly persistent autocorrelation. We study, analytically and by simulation, how this feature of durations generated by the MSMD process propagates to counts and realized volatility. We employ a quasi-maximum likelihood estimator of the MSMD parameters based on the Whittle approximation and establish its strong consistency and asymptotic normality for general MSMD specifications. We show that the Whittle estimation is a computationally simple and fast alternative to maximum likelihood. Finally, we compare the performance of the MSMD model with competing short- and long-memory duration models in an out-of-sample forecasting exercise based on price durations of three major foreign exchange futures contracts. The results of the comparison show that the MSMD and the Long Memory Stochastic Duration model perform similarly and are superior to the short-memory Autoregressive Conditional Duration models.     

On the geometry of multivariate L 1 objective functions

Summary: Two multivariate L ₁ objective functions, namely the k–variate extensions of the classical mean deviation and mean difference, are considered. The duality between the original data vectors and the hyperplanes going through the origin and k – 1 data points is discussed and, consequently, different interesting representations and interpretations of the multivariate mean deviation are introduced. A similar duality is found between the lift data vectors and the hyperplanes going through k data points leading to different representations of the multivariate mean difference. The objective functions are also shown to have interpretations in terms of the centers of facets of the data based zonotopes and lift zonotopes. Moreover, interchanging the roles of the data vectors and the data hyperplanes yields nonparametric measures of (angular) distances between the data vectors as well as between the hyperplanes. Finally, multivariate sign and rank based tests and estimates in the one–sample and several–samples multivariate cases are discussed to illustrate the theory.*The authors wish to thank the referees for valuable comments and suggestions. The research was partially supported by the Academy of Finland.     

Foreign policy priorities and ethnic return migration policies: group-level variation in Greece and Serbia

ABSTRACT

Why do ethnoculturally defined states pursue favourable policies to integrate some returnees from their historical diasporas while neglecting or excluding others? We study this question by looking at members of two historical diasporas that, in the 1990s, returned to their respective ethnic homelands, Greece and Serbia, but were not treated uniformly by their respective governments. Utilising a wide range of primary sources, we consider evidence for a number of plausible explanations for such policy variation, including the economic profile of an ethnic returnee group, its status in internal ethnic hierarchies, its lobbying power, and dynamics of party politics. We find, instead, that the observed variation is best explained by the role that each particular group played in the ruling elites’ ex ante foreign policy objectives. Elites discouraged the repatriation of co-ethnics from parts of the world they still had claims over, by pursuing unfavourable repatriation policies. Conversely, absent a revisionist claim, states adopted favourable repatriation policies to encourage their repatriation and facilitate their integration upon return. Methodologically, the article illustrates the importance of focused comparisons across dyads of states and particular sub-diaspora groups.     

Longitudinal data analysis for generalized linear models with follow‐up dependent on outcome‐related variables

In longitudinal studies, observation times are often irregular and subject‐specific. Frequently they are related to the outcome measure or other variables that are associated with the outcome measure but undesirable to condition upon in the model for outcome. Regression analyses that are unadjusted for outcome‐dependent follow‐up then yield biased estimates. The authors propose a class of inverse‐intensity rate‐ratio weighted estimators in generalized linear models that adjust for outcome‐dependent follow‐up. The estimators, based on estimating equations, are very simple and easily computed; they can be used under mixtures of continuous and discrete observation times. The predictors of observation times can be past observed outcomes, cumulative values of outcome‐model covariates and other factors associated with the outcome. The authors validate their approach through simulations and they illustrate it using data from a supported housing program from the US federal government.