Yazar "Erisoglu, Murat" seçeneğine göre listele

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    An Approach for Determining the Number of Clusters in a Model-Based Cluster Analysis
    (Mdpi Ag, 2017) Akogul, Serkan; Erisoglu, Murat
    To determine the number of clusters in the clustering analysis that has a broad range of applied sciences, such as physics, chemistry, biology, engineering, economics etc., many methods have been proposed in the literature. The aim of this paper is to determine the number of clusters of a dataset in a model-based clustering by using an Analytic Hierarchy Process (AHP). In this study, the AHP model has been created by using the information criteria Akaike's Information Criterion (AIC), Approximate Weight of Evidence (AWE), Bayesian Information Criterion (BIC), Classification Likelihood Criterion (CLC), and Kullback Information Criterion (KIC). The achievement of the proposed approach has been tested on common real and synthetic datasets. The proposed approach based on the corresponding information criteria has produced accurate results. The currently produced results have been seen to be more accurate than those corresponding to the information criteria.
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    A comparison of the parameter estimation methods for bimodal mixture Weibull distribution with complete data
    (Taylor & Francis Ltd, 2015) Karakoca, Aydin; Erisoglu, Ulku; Erisoglu, Murat
    Bimodal mixture Weibull distribution being a special case of mixture Weibull distribution has been used recently as a suitable model for heterogeneous data sets in many practical applications. The bimodal mixture Weibull term represents a mixture of two Weibull distributions. Although many estimation methods have been proposed for the bimodal mixture Weibull distribution, there is not a comprehensive comparison. This paper presents a detailed comparison of five kinds of numerical methods, such as maximum likelihood estimation, least-squares method, method of moments, method of logarithmic moments and percentile method (PM) in terms of several criteria by simulation study. Also parameter estimation methods are applied to real data.
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    Developing a restricted two-parameter Liu-type estimator: A comparison of restricted estimators in the binary logistic regression model
    (Taylor & Francis Inc, 2017) Asar, Yasin; Erisoglu, Murat; Arashi, Mohammad
    In the context of estimating regression coefficients of an ill-conditioned binary logistic regression model, we develop a new biased estimator having two parameters for estimating the regression vector parameter when it is subjected to lie in the linear subspace restriction H = h. The matrix mean squared error and mean squared error (MSE) functions of these newly defined estimators are derived. Moreover, a method to choose the two parameters is proposed. Then, the performance of the proposed estimator is compared to that of the restricted maximum likelihood estimator and some other existing estimators in the sense of MSE via a Monte Carlo simulation study. According to the simulation results, the performance of the estimators depends on the sample size, number of explanatory variables, and degree of correlation. The superiority region of our proposed estimator is identified based on the biasing parameters, numerically. It is concluded that the new estimator is superior to the others in most of the situations considered and it is recommended to the researchers.
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    Heterogeneous data modeling with two-component Weibull-Poisson distribution
    (Taylor & Francis Ltd, 2013) Erisoglu, Ulku; Erisoglu, Murat; Calis, Nazif
    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.
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    Percentile Estimators for Two-Component Mixture Distribution Models
    (Springer International Publishing Ag, 2019) Erisoglu, Ulku; Erisoglu, Murat
    The percentile estimators have a widespread usage in the estimation of distribution parameters because of simplicity and ease of computation. In this study, we investigate the percentile method for two-component mixture distribution models which are commonly used in modeling of heterogeneous univariate data sets. We have proposed percentile estimator for two-component mixture Weibull and two-component mixture Rayleigh distributions according to two different approaches. Performances of the defined percentile estimators were compared with maximum likelihood estimators using simulation. For this purpose, we used several criteria which are bias, mean squared error, mean absolute deviation, mean relative total error and running time of the algorithm. The benefits of the proposed methods have been illustrated by three different real data sets.