Y. Murat Bulut

Y. Murat Bulut
Eskisehir Osmangazi University | ESOGU · Department of Statistics

Phd

About

13
Publications
2,771
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179
Citations
Additional affiliations
December 2010 - January 2016
Eskisehir Osmangazi University
Position
  • Research Assistant

Publications

Publications (13)
Article
This paper deals with the improved point estimations of the inverse power Lindley distribution. The maximum likelihood estimation method, and a simulated annealing algorithm, a heuristic alternative method, are considered. The bias-corrected estimation methods are also used to evaluate the bias reduction of the estimates. These bias-corrected estim...
Article
The inverse Gaussian regression (IGR) model parameters are generally estimated using the maximum likelihood (ML) estimation method. Since the multicollinearity problem exists among the explanatory variables, the ML estimation method becomes inflated. When the multicollinearity problem occurs, biased estimators can be used to estimate the parameters...
Article
It is well known that multicollinearity, which occurs among the explanatory variables, has adverse effects on the maximum likelihood estimator in the inverse Gaussian regression model. Biased estimators are proposed to cope with the multicollinearity problem in the inverse Gaussian regression model. The main interest of this article is to introduce...
Article
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This paper proposes finite mixtures of multivariate skew Laplace distributions in order to model both skewness and heavy-tailedness in heterogeneous data sets. Maximum likelihood estimators for the parameters of interest are obtained using the EM algorithm. The paper offers a small simulation study and a real data example to illustrate the performa...
Article
Full-text available
The t-distribution (univariate and multivariate) has many useful applications in robust statistical analysis. The parameter estimation of the t-distribution is carried out using maximum likelihood (ML) estimation method, and the ML estimates are obtained via the Expectation-Maximization (EM) algorithm. In this article, we will use the maximum Lq-li...
Article
Full-text available
In this paper, we introduce a new distribution as a scale mixture of the generalized half normal (GHN) distribution proposed by Cooray and Ananda (2008) and the generalized gamma (GG) distribution. Since the half-t (HT) distribution given in Wiper et al. (2008) is a special case of the new distribution, we call the new distribution as “generalized...
Article
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The purpose of this study is to propose robust estimators by using optimal B-robust (OBR) estimation method (Hampel et al. [5]) for the parameters of the generalized half-normal (GHN) distribution. After given the robust estimators, we provide a small simulation study to compare its performance with the estimators obtained from maximum likelihood (...
Article
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Finite mixtures of multivariate t distributions (Peel and McLachlan (2000)) were introduced as an alternative to the finite mixtures of multivariate normal distributions to model data sets with heavy tails. In this study, we define the finite mixtures of matrix variate t distributions as an extension of finite mixtures of multivariate t distributio...
Article
Many engineers and scientists concern with future energy demand. They use many different statistical methods to estimate future energy demand such as multiple linear regression, neural networks, genetic algorithms and so on. In this paper, we propose ridge regression (RR) and partial least squares regression (PLSR) methods to estimate future energy...
Article
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A bimodal extension of the generalized gamma distribution is proposed by using a mixing approach. Some distributional properties of the new distribution are investigated. The maximum likelihood (ML) estimators for the parameters of the new distribution are obtained. Real data examples are given to show the strength of the new distribution for model...
Article
In this paper, we introduce a matrix variate slash distribution as a scale mixture of the matrix variate normal and the uniform distributions. We study some properties of the proposed distribution and give maximum likelihood (ML) estimators of its parameters using EM algorithm. We provide an iteratively reweighting algorithm to compute the ML estim...
Article
Full-text available
Weibull distribution has been one of the most widely used distribution to determine potential of wind energy. Many different numerical methods can be used to estimate the parameters of the Weibull distribution. The L-moment method (L-MoM), which has not been used extensively in the previous literature about wind energy for the estimation of wind sp...

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