İstatistik Bilim Dalı

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Adaptive Estimation of Autoregressive Models Under Long-Tailed Symmetric Distribution
(Association for Computing Machinery, 2019) Yengür, Begüm; Bayrak, Özlem Türker; Dener Akkaya, Ayşen; 56416
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.
Assessment of the Use of AutoCAD in Mechanical Engineering Technical Drawing Education
(2017) Akyürek, Turgut
: AutoCAD is one of the widely used software tools in engineering education. In this study, a general assessment of AutoCAD for the usage in the mechanical engineering technical drawing education is made. AutoCAD is assessed in terms of the fulfilment of the requirements defined for the main two technical drawing courses. AutoCAD is assessed in terms of its capability in meeting the requirements of the technical drawing courses
Effect of Estimation on Simple Linear Profile Monitoring under Non-normality
(2019) Aytaçoğlu, Burcu; Türker Bayrak, Özlem; 56416
In recent years, many control charts have been proposed to monitor profiles where the quality of a process/product is expressed as function of response and explanatory variable(s). The methods mostly assume that the in control parameter values are known in Phase II analysis and innovations are normally distributed. However, in practice, the parameters are estimated in Phase I analysis and innovations may be non-normal. In this study, the performance of T2, EWMA-R and EWMA-3 methods for monitoring simple linear profiles is examined via simulation where the parameters are estimated and innovations have Student’s t-distribution. As a performance measure, both the average and standard deviation of the run length is considered. Finally, some recommendations for practitioners are summarized in a table.
Linear Profile Monitoring Adapted to Construct Early Warning System in Economics: A Pilot Study From Energy Sector
(2019) Türker Bayrak, Özlem; Aytaçoğlu, Burcu; Yüksel Haliloğlu, Ebru; 56416
In this study, control charts for monitoring linear profiles are adopted to early warning system (EWS) to see if global crises can be detected before they occur so that preventive actions can be taken by the policy makers. For this purpose, the relation between the annual gross domestic product (GDP) and energy consumption of G8 and big emerging countries through the years 1980-2012 is observed. Phase I analysis indicated that the model parameters are autocorrelated through time. Thus, the Shewhart and EWMA charts for linear profile monitoring are adopted to take this into account and found that EWMA is better. It is seen that the 2008 global crisis can be detected whereas relatively local Asian crisis cannot. This is the first study that integrates linear profile monitoring schemes to EWS and that takes into account the correlation among profiles with different explanatory variables (x-values) for each profile.

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