Minimal conversely consistent extension of the men-optimal solution

This study pertains to two-sided, one-to-one matching problems and considers the best-known solution concept: the men-optimal solution. The men-optimal solution fails to satisfy consistency as well as converse consistency. Furthermore, the minimal consistent extension of the men-optimal solution equals the core. In this article, we compute the minimal conversely consistent extension of the men-optimal solution as a correspondence which associates with each problem the set consisting of the men-optimal matching, and all stable and men-barterproof matchings for this problem.     

Association Between Unemployment Duration and Depression in the SouthWest Region of Turkey: A Cross-Sectional Study

ABSTRACT

Unemployment has risen as an effect of globalization in the country of Turkey. To date, no studies have examined the association between the duration of unemployment and perceived mental health. Therefore, the aim of this study is to evaluate the association between the duration of unemployment and the incidenence of depression among citizens in the southwest region of Turkey. A questionnaire was developed and distributed to 908 unemployed individuals. The questionnaire gathered sociodemographic characteristics and employment-related circumstances. To measure mental health, a depression inventory was administered and group-wise t-tests and logistic regressions were conducted. The findings show that long-term unemployed persons had more episodes of depressive moods in the past 12 months when compared with the group of the short-term unemployed. In addition, depression levels were higher among the long-term unemployed compared with the short-term unemployed. It was estimated that the duration of unemployment and the measurement of depression had a positive correlation. Risk factors that increased depression in the short-term unemployed were gender (female), older age, and greater periods of unemployment. However, higher education, income, and having social insurance significantly decreased the risk of developing depression for both the short-term and the long-term unemployed. Future recommendations include supportive counseling to increase motivation levels and developing strategies to handle periods of unemployment.     

Linking Metatraits of the Big Five to Well-Being and Ill-Being: Do Basic Psychological Needs Matter?

There is considerable evidence that two higher order factors underlie the Big-Five dimensions and that these two factors provide a parsimonious taxonomy. However, not much empirical evidence has been documented as to the extent to which these traits relate to certain psychological constructs. In this study, we tested a structural model to investigate the individual differences in well-being and ill-being by examining the mediating effects of autonomy, relatedness, and competence on the extent to which two higher order factors of personality, namely Stability and Plasticity, are linked to life satisfaction and depression. In testing the model, we controlled for the effects of current affect. A large community sample participated and responded to self-measures of The Big-Five personality, basic psychological needs satisfaction, satisfaction with life, depressive symptoms, and positive and negative affect. The results revealed that satisfaction of basic psychological needs fully mediated the relationship between Plasticity and life satisfaction, as well as depression. It also fully mediated the relationship between Stability and life satisfaction, and partially mediated the relationship between Stability and depression.     

International migration and the NGOs working in the field of migration in Turkey

This study aims to analyse the activities and discourses of the non-governmental organizations (NGOs) working in the field of migration in Turkey. The research explores how the NGOs frame the issue of migration differently and how they define and comprehend migration, migrants, and refugees. The article discusses whether the NGOs view refugees as subjects needing help, support, and protection or as actors who have rights and must be empowered and argues that there is tension between the two approaches. These points are discussed through a field study, conducted in 2020 (between February and April), comprising in-depth interviews with representatives of 13 NGOs working in the field of migration. The study points to structural, historical, and conjectural causes and aspects of the weakness of rights-based attitudes and activities among the NGOs working in the field of migration in Turkey. The article finds out that the actions of the NGOs working in the field of migration are concentrated on the basic needs of refugees, and structural limitations, such as political pressure, political polarization, and capacity problems, push the NGOs to evaluate the issue of migration within relatively narrow frames. The article also stresses the importance of an enabling political and legal environment and a coherent and structured communication strategy to promote a rights-based migration agenda.     

Dummy variables vs. category-wise models

Empirical research frequently involves regression analysis with binary categorical variables, which are traditionally handled through dummy explanatory variables. This paper argues that separate category-wise models may provide a more logical and comprehensive tool for analysing data with binary categories. Exploring different aspects of both methods, we contrast the two with a Monte Carlo simulation and an empirical example to provide a practical insight.     

A Horvitz-Thompson Estimator of the Population Mean Using Inclusion Probabilities of Ranked Set Sampling

In this study, we define the Horvitz-Thompson estimator of the population mean using the inclusion probabilities of a ranked set sample in a finite population setting. The second-order inclusion probabilities that are required to calculate the variance of the Horvitz-Thompson estimator were obtained. The Horvitz-Thompson estimator, using the inclusion probabilities of ranked set sample, tends to be more efficient than the classical ranked set sampling estimator especially in a positively skewed population with small sizes. Also, we present a real data example with the volatility of gasoline to illustrate the Horvitz-Thompson estimator based on ranked set sampling.     

Leverages and Influential Observations in a Regression Model with Autocorrelated Errors

This article deals with the general form of the hat matrix and the DFBETA measure to detect the influential observations and the leverages in the linear regression model with more than one regressor when the errors are from AR(1) and AR(2) processes. Previous studies dealing with the influential observations and the leverages in the constant mean model and regression through the origin model are obtained as special cases. To demonstrate the utility of the hat matrix and the DFBETA measure, two numerical examples based on the ice cream consumption data with AR(1) errors and the Fox-Hartnagel data with AR(2) errors are analyzed. The results show that the parameter of the autoregressive process affects the influential and leverage points.     

Goodness of fit tests for Nakagami distribution based on smooth tests

ABSTRACT

Nakagami distribution is one of the most common distributions used to model positive valued and right skewed data. In this study, we interest goodness of fit problem for Nakagami distribution. Thus, we propose smooth tests for Nakagami distribution based on orthonormal functions. We also compare these tests with some classical goodness of fit tests such as Cramer–von Mises, Anderson–Darling, and Kolmogorov–Smirnov tests in respect to type-I error rates and powers of tests. Simulation study indicates that smooth tests give better results than these classical tests give in respect to almost all cases considered.     

Concurrent processing of heteroskedastic vector-valued mixture density models

We introduce a combined two-stage least-squares (2SLS)–expectation maximization (EM) algorithm for estimating vector-valued autoregressive conditional heteroskedasticity models with standardized errors generated by Gaussian mixtures. The procedure incorporates the identification of the parametric settings as well as the estimation of the model parameters. Our approach does not require a priori knowledge of the Gaussian densities. The parametric settings of the 2SLS_EM algorithm are determined by the genetic hybrid algorithm (GHA). We test the GHA-driven 2SLS_EM algorithm on some simulated cases and on international asset pricing data. The statistical properties of the estimated models and the derived mixture densities indicate good performance of the algorithm. We conduct tests on a massively parallel processor supercomputer to cope with situations involving numerous mixtures. We show that the algorithm is scalable.