{"id":790,"date":"2019-07-03T21:59:14","date_gmt":"2019-07-03T19:59:14","guid":{"rendered":"https:\/\/www.hsu-hh.de\/statistik\/?post_type=tribe_events&#038;p=790"},"modified":"2019-07-16T13:58:47","modified_gmt":"2019-07-16T11:58:47","slug":"effect-of-estimation-under-non-normality","status":"publish","type":"tribe_events","link":"https:\/\/www.hsu-hh.de\/statistik\/event\/effect-of-estimation-under-non-normality","title":{"rendered":"Burcu Aytacoglu (Ege University)"},"content":{"rendered":"<h1>Effect of estimation under non-normality on the phase II performance of linear profile monitoring approaches<\/h1>\n<p>Recently, there have been several studies about control charts to monitor profiles, where the quality of a process\/product is expressed as function of response and explanatory variable(s). Mostly, it is assumed that the in-control parameter values are known and the error terms are normally distributed. However, these assumptions are rarely satisfied in practice. In this study, we focused on three popular methods (EWMA-R, EWMA-3, and EWMA3(d2)) for monitoring simple linear profiles and the performance of them is examined via simulation where the in-control parameters are estimated and error terms have a Student\u2019s t distribution or gamma distribution. In order to capture the sampling variation among different practitioners, average and standard deviation of the average run length (<abbr title=\"average run length\">ARL<\/abbr>) are used as performance measures. In conclusion, it is seen that the estimation effect becomes more severe as the error term distribution deviates from normality to a greater extent. In addition, although the average <abbr title=\"average run length\">ARL<\/abbr> 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Effect of estimation under non-normality on the phase II performance of linear profile monitoring approaches Recently, there have been several studies about control charts to monitor profiles, where the quality [&hellip;]<\/p>\n","protected":false},"author":335,"featured_media":0,"template":"","meta":{"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[44],"class_list":["post-790","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-kolloquium-de","cat_kolloquium-de"],"_links":{"self":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events\/790","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/users\/335"}],"version-history":[{"count":1,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events\/790\/revisions"}],"predecessor-version":[{"id":791,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events\/790\/revisions\/791"}],"wp:attachment":[{"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/media?parent=790"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tags?post=790"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.hsu-hh.de\/statistik\/wp-json\/wp\/v2\/tribe_events_cat?post=790"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}