İstatistik Bilim Dalı

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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.
Effect of estimation under nonnormality on the phase II performance of linear profile monitoring approaches
(Wiley, 2019) Aytaçoğlu, Burcu; Bayrak, Özlem Türker; 56416
The number of studies about control charts proposed to monitor profiles, where the quality of a process/product is expressed as function of response and explanatory variable(s), has been increasing in recent years. However, most authors assume that the in-control parameter values are known in phase II analysis and the error terms are normally distributed. These assumptions are rarely satisfied in practice. In this study, the performance of EWMA-R, EWMA-3, and EWMA-3(d(2)) methods for monitoring simple linear profiles is examined via simulation where the in-control parameters are estimated and innovations have a Student's t distribution or gamma distribution. Instead of the average run length (ARL) and the standard deviation of run length, we used average and standard deviation of the ARL as performance measures in order to capture the sampling variation among different practitioners. It is seen that the estimation effect becomes more severe when the number of phase I profiles used in estimation decreases, as expected, and as the distribution deviates from normality to a greater extent. Besides, although the average ARL values get closer to the desired values as the amount of phase I data increases, their standard deviations remain far away from the acceptable level indicating a high practitioner-to-practitioner variability.
Global Krizler için Doğrusal Profillere Dayalı Kontrol Şemaları ile Oluşturulan Erken Uyarı Sistemi
(2015) Türker Bayrak, Özlem; Aytaçoğlu, Burcu; Yüksel Haliloğlu, Ebru; 56416
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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Browsing İstatistik Bilim Dalı by Author "Aytaçoğlu, Burcu"