Ilhan Usta

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.

Different estimation methods for the parameters of the extended Burr XII distribution

The extended three-parameter Burr XII (EBXII) distribution has recently attracted considerable attention for modeling data from various scientific fields since it yields a wide range of skewness and kurtosis values. However, it is well known that the parameter estimates have significant effects on the success of a distribution in real-life applications. In this study, modified moment estimators (MMEs) and modified probability-weighted moments estimators (MPWMEs) are used to estimate the parameters of the EBXII distribution. These two considered estimators are also compared with the commonly used maximum-likelihood, percentiles, least-squares and weighted least-squares estimators in terms of bias and efficiency via an extensive numerical simulation. The MMEs and MPWMEs are observed to perform well in varying sample cases, and the simulation results are supported with application through a real-life data set.

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.

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.

Analysis of the Modified Weibull Distribution for Estimation of Wind Speed Distribution

Finding suitable wind speed distribution is one of the most important tasks for the correct estimation of wind energy potential of the specified region. The Weibull distribution is the well-known and suggested distribution in estimating wind energy potential. However, in order to predict wind energy more correctly, new and flexible statistical distributions have been proposed in the last decade. In this study, we introduce a new modified Weibull distribution (MWD) for modelling wind speed and estimating wind power density and also we test MWD versus the classical Weibull distribution on real wind speed data measured in various regions of Turkey. The results of the analysis indicate that MWD suits well for most of the examined wind data cases. Therefore, MWD can be alternatively used for assessment of wind energy potential.