Yeliz Mert Kantar

A comparative study for the location and scale parameters of the Weibull distribution with given shape parameter

Nine parametric estimators of the location and scale parameters of a two-parameter Weibull distribution are compared in terms of their bias and efficiency in a simulation study. The estimators considered are the maximum likelihood estimators (MLE), moment estimators (ME), generalized spacing estimators (GSE), modified maximum likelihood estimators I (MMLE-I), modified maximum likelihood estimators II (MMLE-II), Tikus modified maximum likelihood estimators (TMMLE), least-squares estimators (LSE), weighted least-squares estimators (WLSE) and percentile estimators (PCE). The aim of the comparisons is to identify the most efficient estimators among these nine estimators for different shape parameters and sample sizes.

Mean-Variance-Skewness-Entropy Measures: A Multi-Objective Approach for Portfolio Selection

In this study, we present a multi-objective approach based on a mean-variance-skewness-entropy portfolio selection model (MVSEM). In this approach, an entropy measure is added to the mean-variance-skewness model (MVSM) to generate a well‑diversified portfolio. Through a variety of empirical data sets, we evaluate the performance of the MVSEM in terms of several portfolio performance measures. The obtained results show that the MVSEM performs well out-of sample relative to traditional portfolio selection models.

Comparison of certain value-at-risk estimation methods for the two-parameter Weibull loss distribution

The Weibull distribution is one of the most important distributions that is utilized as a probability model for loss amounts in connection with actuarial and financial risk management problems. This paper considers the Weibull distribution and its quantiles in the context of estimation of a risk measure called Value-at-Risk (VaR). VaR is simply the maximum loss in a specified period with a pre-assigned probability level. We attempt to present certain estimation methods for VaR as a quantile of a distribution and compare these methods with respect to their deficiency (Def) values. Along this line, the results of some Monte Carlo simulations, that we have conducted for detailed investigations on the efficiency of the estimators as compared to MLE, are provided.

A Monte Carlo simulation study on partially adaptive estimators of linear regression models

This paper presents a comprehensive comparison of well-known partially adaptive estimators (PAEs) in terms of efficiency in estimating regression parameters. The aim is to identify the best estimators of regression parameters when error terms follow from normal, Laplace, Students t, normal mixture, lognormal and gamma distribution via the Monte Carlo simulation. In the results of the simulation, efficient PAEs are determined in the case of symmetric leptokurtic and skewed leptokurtic regression error data. Additionally, these estimators are also compared in terms of regression applications. Regarding these applications, using certain standard error estimators, it is shown that PAEs can reduce the standard error of the slope parameter estimate relative to ordinary least squares.

On the performance of the flexible maximum entropy distributions within partially adaptive estimation

The partially adaptive estimation based on the assumed error distribution has emerged as a popular approach for estimating a regression model with non-normal errors. In this approach, if the assumed distribution is flexible enough to accommodate the shape of the true underlying error distribution, the efficiency of the partially adaptive estimator is expected to be close to the efficiency of the maximum likelihood estimator based on knowledge of the true error distribution. In this context, the maximum entropy distributions have attracted interest since such distributions have a very flexible functional form and nest most of the statistical distributions. Therefore, several flexible MaxEnt distributions under certain moment constraints are determined to use within the partially adaptive estimation procedure and their performances are evaluated relative to well-known estimators. The simulation results indicate that the determined partially adaptive estimators perform well for non-normal error distributions. In particular, some can be useful in dealing with small sample sizes. In addition, various linear regression applications with non-normal errors are provided.

Distributions of wind speed at different heights

In this study, the parameters of different distributions such as the Weibull, Rayleigh, Lognormal, Gamma and Generalized Gamma, which are used for modelling wind speed, at the different heights 10 and 30 m, have been evaluated. The monthly and annual variations of the scale parameters of the distributions and performances of the distributions for the wind speed over these different heights have been analyzed. The results have revealed that, the best distribution at 10 m also provides good fit to wind speed observation at 30 m. Moreover, it is observed from results that according to goodness-of-fit tests, the Gamma, Generalized Gamma and Weibull distributions are found to be more suitable than the other considered distributions for representing the actual wind speed data for both heights.

Spatial Correlation Analysis of Unemployment Rates in Turkey

Unemployment is a major problem in Turkey as well as in almost all other countries of the World. Unemployment is defined as the situation of being without a job. A decrease in the growth of economies is a major cause of rising unemployment. (Chowdhuryn and Hossain, 2014). According to economic theory, although the unemployment rate is regarded as an important indicator of labor market performance, there are many other indicators affecting unemployment. Some are listed as the value of imports and exports, the dollar cost of imports and exports, the exchange rate of imports and exports, the exchange rate, population growth, gross national product (GNP) growth at current prices, GNP growth at fixed prices, public investments, private investments and GNP deflator (Goktas and Isci, 2010). Studies regarding the unemployment rate for Turkey generally consider determining the relationship between unemployment and other indicators or variables. For example, Bildirici et al., (2012) investigate unemployment generating effects. Kabaklarli et al., (2011) analyze the economic determinants of the unemployment problem in Turkey. Abstract

A statistical investigation on wind energy potential of Northwest of Turkey

The northwest of Turkey is an important region which is a rapidly developing industrial center. Thus, energy analysis of this region has a high importance for the regions geopolitical, economic and demographic structure. In this study, wind energy potential of three regions in northwest of Turkey is evaluated using wind speed data collected from three stations. The Weibull, Rayleigh and generalized Gamma distributions are used for modeling wind speed data and estimating wind power. It is concluded from analyses that one of the mentioned regions has moderate level wind power potential which can be adequate for mechanical energy applications such as local consumption, agricultural applications and water pumping, other two regions are fairly good locations in terms of wind generation potential.

Evaluation of Robust Estimation Methods in Estimating Weibull Parameters for Wind Energy Application

Two-parameter Weibull distribution has been widely-used reference distribution in wind energy studies and thus its parameter estimation methods have been well-studied in the literature. However, the literature have generally focused on non-robust methods which produce unreliable results in the cases of wind speed data with outliers. In this study, we deal with robust estimation methods of the Weibull distribution for wind energy applications. The considered robust methods are evaluated for both clear and contaminated real wind data cases. It was found that the considered robust methods provides reliable results when it is taken into account in the case of real wind speed data cases. Also, the certain robust methods for the Weibull distribution yield less mean power density error than classical methods in the case of wind speed data with outliers. As a result, it is deduced from analysis that robust methods can be simultaneously used with efficient estimators to check the estimated reliability of the results on wind power.

Robust Estimation for Parameters of the Extended Burr Type III Distribution

We consider various robust estimators for the extended Burr Type III (EBIII) distribution for complete data with outliers. The considered robust estimators are M-estimators, least absolute deviations, Theil, Siegels repeated median, least trimmed squares, and least median of squares. Before we perform the aforementioned estimators for the EBIII, we adapt the quantiles method to the estimation of the shape parameter k of the EBIII. The simulation results show that the considered robust estimators generally outperform the existing estimation approaches for data with upper outliers, with certain of them retaining a relatively high degree of efficiency for small sample sizes.