Improved two-parameter estimators for the negative binomial and Poisson regression models

Negative binomial regression (NBR) and Poisson regression (PR) applications have become very popular in the analysis of count data in recent years. However, if there is a high degree of relationship between the independent variables, the problem of multicollinearity arises in these models. We introduce new two-parameter estimators (TPEs) for the NBR and the PR models by unifying the two-parameter estimator (TPE) of Özkale and Kaç?ranlar [The restricted and unrestricted two-parameter estimators. Commun Stat Theory Methods. 2007;36:2707–2725]. These new estimators are general estimators which include maximum likelihood (ML) estimator, ridge estimator (RE), Liu estimator (LE) and contraction estimator (CE) as special cases. Furthermore, biasing parameters of these estimators are given and a Monte Carlo simulation is done to evaluate the performance of these estimators using mean square error (MSE) criterion. The benefits of the new TPEs are also illustrated in an empirical application. The results show that the new proposed TPEs for the NBR and the PR models are better than the ML estimator, the RE and the LE.     

Reinforcement and erosion of traditional gender roles among young people in a poor metropolitan area of Turkey

This paper aims to contribute to the existing studies on the young women’s transition to adulthood in the case of Turkish women’s exclusion from the labour market. The issue is discussed largely within the boundaries of traditional gender restrictions that keep women out of employment. Herein, we argue that the gender-based control of young women, perceived as the reason for their exclusion from the labour market, may be used by disadvantaged women as a way of avoiding difficult and unfavourable employment situations. Similarly, the gender roles imposed on men could also not be fulfilled due to the same restrictive labour market conditions. Furthermore, we suggest that the poverty simultaneously strengthens and erodes the very base of the traditional gender structure for both young women and young men, while forcing the young women out of employment and the young men to the unstable working conditions. This argument is based on a study conducted in Alt?nda?, Ankara with a sample of 279 females and 340 males aged between 15 and 24. A mixed methodology, comprising a survey questionnaire followed by 60 in-depth interviews and 4 focus group discussions, was employed for this study.     

The effects of perceived quality of organizationally provided meal service on employees’ job performance and mediating role of organizational identification

Contemporary managers are developing alternative ways to sustain their competitive advantages. Motivation-enhancing features such as high-quality meal service provided by organizations can be thought of as one of these methods. The effect of the perceived quality of meal service provided by organizations on the job performance of employees and the mediating role of organizational identification during this process is studied. Private security-sector employees working in Turkey (N?=?597) are being focused in this study. As a result of analyses (correlation and regression analyses, structural equation model), the authors find that the perceived quality of meal service provided by organizations improves employees’ job performance significantly and organizational identification has a partial mediating role on this association. The findings of this study will be of interest to key stakeholders such as public or private organizations, labor unions and the media.     

Accommodating ‘foreigners’: narrative accounts of Berlin's German-born Turkish Ausländer

This paper examines the discursive messages conveyed through everyday encounters among German-born Turkish Ausländer (foreigners) and ethnic Germans in Berlin. Using narrative analysis, I explore how these messages operate to maintain a series of separate and unequal social relations based on gender, ethnicity, religion, and skin colour. The personal experiences of local residents illustrate how these discourses serve to mark some Ausländer as less threatening than others, some as preferable, and others as undesirable. Some Ausländer are also considered more valuable than others, and the status of one often depends on the superior or subordinate status of the other. Through exploring these complexities as experienced by local informants, this paper shows the importance of certain interactive processes that construct Ausländerness in varying ways, while still defining people's identities in relation to norms that privilege the dominant subjects.     

Separate Ratio Estimators for the Population Variance in Stratified Random Sampling

We propose separate ratio estimators for population variance in stratified random sampling. We obtain mean square error equations and compare proposed estimators about efficiency with each other. By these comparisons, we find the conditions which make proposed estimators more efficient than others. It has been shown that proposed classes of estimators are more efficient than usual unbiased estimator. We find that separate ratio estimators are more efficient than combined ratio estimators for population variance. The theoretical results are supported by a numerical illustration with original data. A simulation study is also carried out to investigate empirical performance of estimators.     

Marriage migration: just another case of positive assortative matching?

It is a stylized fact that marriage formation involves positive assortative matching on education. We also find this in the case of immigrants, even when they tend to “import” their spouses and potentially trade off education for other favorable characteristics. For Pakistanis, we find positive compensating differentials in terms of high education to youth having adopted host country norms, when marrying a marriage migrant. This indicates that Pakistani marriage migrants pay a premium to be able to marry and live in Denmark. For Turks, individuals having source country norms pay a premium in order to import a partner, indicating that unspoiled traditional norms are traded off for education.

Heterogeneous data modeling with two-component Weibull–Poisson distribution

The mixture distribution models are more useful than pure distributions in modeling of heterogeneous data sets. The aim of this paper is to propose mixture of Weibull–Poisson (WP) distributions to model heterogeneous data sets for the first time. So, a powerful alternative mixture distribution is created for modeling of the heterogeneous data sets. In the study, many features of the proposed mixture of WP distributions are examined. Also, the expectation maximization (EM) algorithm is used to determine the maximum-likelihood estimates of the parameters, and the simulation study is conducted for evaluating the performance of the proposed EM scheme. Applications for two real heterogeneous data sets are given to show the flexibility and potentiality of the new mixture distribution.     

Robust estimation and testing in one-way ANOVA for Type II censored samples: skew normal error terms

Censoring can be occurred in many statistical analyses in the framework of experimental design. In this study, we estimate the model parameters in one-way ANOVA under Type II censoring. We assume that the distribution of the error terms is Azzalini's skew normal. We use Tiku's modified maximum likelihood (MML) methodology which is a modified version of the well-known maximum likelihood (ML) in the estimation procedure. Unlike ML methodology, MML methodology is non-iterative and gives explicit estimators of the model parameters. We also propose new test statistics based on the proposed estimators. The performances of the proposed estimators and the test statistics based on them are compared with the corresponding normal theory results via Monte Carlo simulation study. A real life data is analysed to show the implementation of the methodology presented in this paper at the end of the study.     

A new per-field classification method using mixture discriminant analysis

In this study, a new per-field classification method is proposed for supervised classification of remotely sensed multispectral image data of an agricultural area using Gaussian mixture discriminant analysis (MDA). For the proposed per-field classification method, multivariate Gaussian mixture models constructed for control and test fields can have fixed or different number of components and each component can have different or common covariance matrix structure. The discrimination function and the decision rule of this method are established according to the average Bhattacharyya distance and the minimum values of the average Bhattacharyya distances, respectively. The proposed per-field classification method is analyzed for different structures of a covariance matrix with fixed and different number of components. Also, we classify the remotely sensed multispectral image data using the per-pixel classification method based on Gaussian MDA.