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    Gambling behavior of husbands of married women living in Turkey and risk factors
    (Taylor & Francis Inc, 2023) Guney, Esra; Alkan, Omer; Genc, Asir; Kabakus, Ahmet Kamil
    Objectives Gambling is a problem that has dated back to early history, across the world and in most cultures. Gambling behavior has a lot of negative health, social and environmental effects. The purpose of this study is to determine the risk factors influencing the gambling behavior of husbands of married women in Turkey. Method This study utilized a micro dataset from National Research on Domestic Violence against Women in Turkey conducted by Hacettepe University Institute of Population Studies in 2014. The factors affecting on the gambling behavior of the husbands of married women were determined by gompit and binary logistic regression analyses. Results The analysis results concluded that the best model was the gompit regression model. The findings from the application of this model suggested that variables of regional differences, household welfare level, men's alcohol and drug use influenced the gambling behavior of married women's husbands. Conclusion In order to develop preventive strategies to reduce gambling behavior, individuals living in Southern Turkey and households with low-income should be primarily addressed, and men who use alcohol and drugs, should be addressed as well.
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    New Shrinkage Parameters for the Liu-type Logistic Estimators
    (Taylor & Francis Inc, 2016) Asar, Yasin; Genc, Asir
    The binary logistic regression is a widely used statistical method when the dependent variable has two categories. In most of the situations of logistic regression, independent variables are collinear which is called the multicollinearity problem. It is known that multicollinearity affects the variance of maximum likelihood estimator (MLE) negatively. Therefore, this article introduces new shrinkage parameters for the Liu-type estimators in the Liu (2003) in the logistic regression model defined by Huang (2012) in order to decrease the variance and overcome the problem of multicollinearity. A Monte Carlo study is designed to show the goodness of the proposed estimators over MLE in the sense of mean squared error (MSE) and mean absolute error (MAE). Moreover, a real data case is given to demonstrate the advantages of the new shrinkage parameters.
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    A New Two-Parameter Estimator for the Poisson Regression Model
    (Springer International Publishing Ag, 2018) Asar, Yasin; Genc, Asir
    It is known that multicollinearity affects the maximum likelihood estimator (MLE) negatively when estimating the coefficients in Poisson regression. Namely, the variance of MLE inflates and the estimations become instable. Therefore, in this article we propose a new two-parameter estimator (TPE) and some methods to estimate these two parameters for the Poisson regression model when there is multicollinearity problem. Moreover, we conduct a Monte Carlo simulation to evaluate the performance of the estimators using mean squared error (MSE) criterion. We finally consider a real data application. The simulations results show that TPE outperforms MLE in almost all the situations considered in the simulation and it has a smaller MSE and smaller standard errors than MLE in the application.
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    A note on some new modifications of ridge estimators
    (Academic Publication Council, 2017) Asar, Yasin; Genc, Asir
    Ridge estimator is an alternative to ordinary least square estimator, when there is multicollinearity problem. There are many proposed estimators in literature. In this paper, we propose some new estimators. A Monte Carlo experiment has been conducted for the comparison of the performances of the estimators. Mean squared error (MSE) is used as a performance criterion. The benefits of new estimators are illustrated using a real dataset. According to both simulation results and application, our new estimators have better performances in the sense of MSE in most of the situations.
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    Two-parameter ridge estimator in the binary logistic regression
    (Taylor & Francis Inc, 2017) Asar, Yasin; Genc, Asir
    The binary logistic regression is a commonly used statistical method when the outcome variable is dichotomous or binary. The explanatory variables are correlated in some situations of the logit model. This problem is called multicollinearity. It is known that the variance of the maximum likelihood estimator (MLE) is inflated in the presence of multicollinearity. Therefore, in this study, we define a new two-parameter ridge estimator for the logistic regression model to decrease the variance and overcome multicollinearity problem. We compare the new estimator to the other well-known estimators by studying their mean squared error (MSE) properties. Moreover, a Monte Carlo simulation is designed to evaluate the performances of the estimators. Finally, a real data application is illustrated to show the applicability of the new method. According to the results of the simulation and real application, the new estimator outperforms the other estimators for all of the situations considered.