Yazar "Asar, Yasin" seçeneğine göre listele

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    Liu type logistic estimators
    (Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2015-01-08) Asar, Yasin; Genç, Aşır
    Binari lojistik regresyon modellerinde çoklu bağlantı problemi en çok olabilirlik tahmin edicisinin varyansını şişirmekte ve tahmin edicinin performansını düşürmektedir. Bu nedenle doğrusal modellerde çoklu bağlantı problemini gidermek için önerilen tahmin ediciler lojistik regresyona genelleştirilmiştir. Bu tezde, bazı yanlı tahmin edicilerin lojistik versiyonları gözden geçirilmiştir. Ayrıca, yeni bir genelleştirme yapılarak daha öncekilerle MSE kriteri bakımından performansı karşılaştırılmıştır. Yeni önerilen tahmin edici iki parametreli olduğundan parametrelerin seçimi için tekrarlı (iterative) bir metot önerilmiştir.
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    Modified ridge regression parameters: A comparative Monte Carlo study
    (HACETTEPE UNIV, FAC SCI, 2014) Asar, Yasin; Karaibrahimoglu, Adnan; Genc, Asir
    In multiple regression analysis, the independent variables should be uncorrelated within each other. If they are highly intercorrelated, this serious problem is called multicollinearity. There are several methods to get rid of this problem and one of the most famous one is the ridge regression. In this paper, we will propose some modified ridge parameters. We will compare our estimators with some estimators proposed earlier according to mean squared error (MSE) criterion. All results are calculated by a Monte Carlo simulation. According to simulation study, our estimators perform better than the others in most of the situations in the sense of MSE.
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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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    Statistical Modeling of Seismicity of Van Region by Using SETAR Model
    (Selçuk Üniversitesi, 2012) Tekşen, Ümran Kahraman; Asar, Yasin; Başbozkurt, Hakan; Akoğul, Serkan; Genç, Asır
    In this study, earthquake data between 24.01.1905 and 18.04.2012 of the Van region is tried to be modeled by using self-exciting threshold autoregressive (SETAR) model. In this study, constructive parameters are determined by using nonlinear-threshold test suggested by Tsay (1989). Conclusively, estimating values are obtained for the earthquake data.
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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.