Ahmet Akkaya

E-mail

aakkaya@metu.edu.tr

Department

Department of Political Science and Public Administration

ORCID

0000-0002-7156-3789

Web of Science Researcher ID

ABA-3215-2020

Robust estimation of magnitude-frequency relationship parameters Yücemen, Mehmet Semih; Akkaya, Ahmet (2012-01-01) The precise estimation of the a and b parameters of Richter's magnitude-frequency relationship is of primary importance, since the evaluation of seismicity and assessment of seismic hazard depend on these two parameters. I...
Estimating parameters of a multiple autoregressive model by the modified maximum likelihood method TÜRKER BAYRAK, ÖZLEM; Akkaya, Ahmet (Elsevier BV, 2010-02-15) We consider a multiple autoregressive model with non-normal error distributions, the latter being more prevalent in practice than the usually assumed normal distribution. Since the maximum likelihood equations have converg...
Estimating parameters in autoregressive models with asymmetric innovations Akkaya, Ahmet; Tiku, Moti L. (Elsevier BV, 2008-12-01)
Short-tailed distributions and inliers Akkaya, Ahmet; Akkaya, Ayşen (Springer Science and Business Media LLC, 2008-08-01) We consider two families of short-tailed distributions (kurtosis less than 3) and discuss their usefulness in modeling numerous real life data sets. We develop estimation and hypothesis testing procedures which are efficie...
Analysis of variance and linear contrasts in experimental design with generalized secant hyperbolic distribution Yilmaz, Yidiz E.; Akkaya, Ahmet (Elsevier BV, 2008-07-01) We consider one-way classification model in experimental design when the errors have generalized secant hyperbolic distribution. We obtain efficient and robust estimators for block effects by using the modified maximum lik...
Robust estimation in multiple linear regression model with non-Gaussian noise Akkaya, Ahmet; Tiku, Moti L. (2008-02-01) The traditional least squares estimators used in multiple linear regression model are very sensitive to design anomalies. To rectify the situation we propose a reparametrization of the model. We derive modified maximum lik...

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