# Yesim GuneyAnkara University · Department of Statistics

Yesim Guney

PhD

## About

23

Publications

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61

Citations

## Publications

Publications (23)

Magnesite is an ore used in the production of a wide variety of industrial minerals and compounds and magnesium metal, as well as its alloys. The main components of magnesite are MgO and CO2. However, magnesite, which is not generally observed in nature as pure, contains certain amounts of SiO2, CaO, and Fe2O3. The loss on ignition (LOI) value in m...

In the linear regression model with possibly autoregressive errors, we construct a family of nonparametric tests for significance of regression, under a nuisance autoregression of model errors. The tests avoid an estimation of nuisance parameters, in contrast to the tests proposed in the literature. A simulation study illustrate their good performa...

In countries with a severe outbreak of Covid-19, most governments are considering whether anti-transmission measures are worth social and economic costs. The seriousness of economic costs such as the closure of some workplaces, unemployment, reduction in production, and social costs such as school closures, disruptions in education could be observa...

In the analysis of repeated or clustered measurements, it is crucial to determine the dynamics that affect the mean, variance, and correlations of the data, which will be possible using appropriate models. One of these models is the joint mean-covariance model, which is a multivariate heteroscedastic regression model with autoregressive covariance...

Spreading of novel coronavirus disease started in China and moved to Korea and Japan, then several countries in Europe, and the last step to the countries in the North and South American continents. Since the virus spread worldwide, we simultaneously use all available daily confirmed cases, recovered cases, and death data to cluster countries in ti...

In the analysis of repeated or clustered measurements, it is crucial to determine the dynamics which affect the mean, variance, and correlations of the data, which will be possible with the use of appropriate models. One of these models is the joint mean-covariance models, which can be considered as multivariate heteroscedastic regression models wi...

The assumption of equal variances is not always appropriate and different approaches for modelling variance heterogeneity have been widely studied in the literature. One of these approaches is joint location and scale model defined with the idea that both the location and the scale depend on explanatory variables through parametric linear models. B...

In recent years, in the literature of linear regression models, robust model selection methods have received increasing attention when the datasets contain even a small fraction of outliers. Outliers can have a serious impact on statistical inference and the choice of models using model selection criteria. Most of the existing robust information-ba...

Linear regression models are useful statistical tools to analyze data sets in different fields. There are several methods to estimate the parameters of a linear regression model. These methods usually perform under normally distributed and uncorrelated errors. If error terms are correlated the Conditional Maximum Likelihood (CML) estimation method...

In this article, we consider the parameter estimation of regression model with pth order autoregressive (AR(p)) error term. We use the Maximum Lq-likelihood (MLq) estimation method that is proposed by Ferrari and Yang (2010a), as a robust alternative to the classical maximum likelihood (ML) estimation method to handle the outliers in the data. Afte...

In countries with a severe outbreak of COVID-19, most governments are considering whether anti-transmission measures are worth social and economic costs. The seriousness of economic costs such as the closure of some workplaces, unemployment, reduction in production, and social costs such as school closures, disruptions in education could be observa...

In this paper we study several competing models under general class of skew-t distributions. Namely. we consider joint location and scale model (JLSM) under Student’s t and under skew-t distributions, respectively. Similarly, we consider the extension of JLSM to joint location-scale and skewness model (JLSSM) under skew-t distribution in heterosced...

Linear regression models are useful statistical tools to analyze data sets in several different fields. There are several methods to estimate the parameters of a linear regression model. These methods usually perform under normally distributed and uncorrelated errors with zero mean and constant variance. However, for some data sets error terms may...

In the linear regression model with possibly autoregressive errors, we propose a family of nonparametric tests for regression under a nuisance autoregression. The tests avoid the estimation of nuisance parameters, in contrast to the tests proposed in the literature.

This paper considers the averaged autoregression quantile in autoregressive models. Our primary interest is its structure, qualities, and its applications. Moreover, under the local heteroscedasticity we investigate the properties of averaged autoregression quantile. For an illustration, a simulation study is provided.

The methodology of automatic method selection (metalearning) allows to recommend the most suitable method (e.g. algorithm or statistical estimator) from several alternatives for a given dataset, based on information learned over a training database of datasets. Practitioners have become accustomed to using metalearning in the context of regression...

In the linear regression model, the errors are usually assumed to be uncorrelated. However, in real-life data, this assumption is not often plausible. In this study, first, we will assume that the errors of the regression model have autoregressive structure. This type of regression models has been considered before. However, in those papers under t...

Marshall-Olkin Extended Burr XII (MOEBXII) distribution family, which is a generalization of Burr XII distribution proposed by Al-Saiari et al. [1] , is a flexible distribution that can be used in many fields such as actuarial science, economics, life testing, reliability and failure time modeling. The parameters of the MOEBXII distribution are usu...

Marshall-Olkin extended Burr XII (MOEBXII) distribution is proposed by Al-Saiari et al. (2014) to obtain a more flexible family of distributions. Some estimation methods like maximum likelihood, Bayes and M estimations are used to estimate the parameters of the MOEBXII distribution in literature. In this paper, we propose to use Maximum Lq (MLq) es...

In this paper, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy tailed alternative to the normal distribution. We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorit...

In this study, as alternatives to the maximum likelihood (ML) and the frequency estimators, we propose robust estimators for the parameters of Zipf and Marshall–Olkin Zipf distributions. A small simulation study is given to illustrate the performance of the proposed estimators. We apply the proposed estimators to a real data set from cancer researc...