İstatistik Bilim Dalı Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/4382

Browse

Now showing 1 - 3 of 3

Adaptive Estimation of Autoregressive Models Under Long-Tailed Symmetric Distribution
(Association for Computing Machinery, 2019) Yentür, B.; Bayrak, Ö.T.; Akkaya, A.D.
In this paper, we consider the autoregressive models where the error term is non-normal; specifically belongs to a long-tailed symmetric distribution family since it is more relevant in practice than the normal distribution. It is known that least squares (LS) estimators are neither efficient nor robust under non-normality and maximum likelihood (ML) estimators cannot be obtained explicitly and require a numerical solution which might be problematic. In recent years, modified maximum likelihood (MML) estimation is developed to overcome these difficulties. However, this method requires that the shape parameter is known which is not realistic in machine data processing. Therefore, we use adaptive modified maximum likelihood (AMML) technique which combines MML with Huber’s estimation procedure so that the shape parameter is also estimated. After derivation of the AMML estimators, their efficiency and robustness properties are discussed through a simulation study and compared with MML and LS estimators. © 2019 Association for Computing Machinery.
Citation - WoS: 2
Citation - Scopus: 1
Inter-Laboratory Comparison Scheme for Fuel Sector, Labkar in Turkey
(Springer, 2009) Bayrak, Ozlem Turker; Okandan, Ender; Uckardes, Hale
Fuel sector is one of the powerful sectors in Turkish industry. The implementation of a new law for regulating the fuel sector had enforced the quality control of fuels sold to public. This resulted in several accredited fuel-testing laboratories to emerge. Thus, a scheme to evaluate their proficiency in measurements became an important requirement. The inter-laboratory comparison scheme LABKAR for gasoline, diesel oil, LPG, lubricating oil and biodiesel samples have evolved to fulfill this need. In this paper, LABKAR is introduced; the results obtained from the program are analyzed and discussed. The kernel densities of the participants' results show that the use of robust mean as a consensus value is appropriate for fuel samples. Although the number of rounds is not enough to derive strict conclusions, it is seen that the performance of the scheme based on the standard deviations and coefficient of variations is improving in each round. It has been observed that the number of laboratories receiving "action" or "warning" is decreasing, which indicates that they are benefiting from the scheme.
Citation - Scopus: 1
Linear Contrasts in One-Way Classification Ar(1) Model With Gamma Innovations
(Hacettepe Univ, Fac Sci, 2016) Senoglu, Birdal; Bayrak, Ozlem Turker
In this study, the explicit estimators of the model parameters in oneway classification AR(1) model with gamma innovations are derived by using modified maximum likelihood (MML) methodology. We also propose a new test statistic for testing linear contrasts. Monte Carlo simulation results show that the MML estimators have higher efficiencies than the traditional least squares (LS) estimators and the proposed test has much better power and robustness properties than the normal theory test.

Browse

Browsing İstatistik Bilim Dalı Yayın Koleksiyonu by Scopus Q "Q3"