پديد آورنده :
حساميان، غلامرضا
عنوان :
استنباط آماري ناپارامترهاي بر اساس اطلاعات نادقيق
گرايش تحصيلي :
آمار رياضي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده علوم رياضي
صفحه شمار :
نه،118ص.: مصور،جدول،نمودار
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
محمود طاهري
استاد مشاور :
ماشاءالله ماشين چي
توصيفگر ها :
رده فازي , داده فازي , مقدار بحراني فازي , تابع توزيع فازي
تاريخ نمايه سازي :
28/11/91
استاد داور :
بهرام صادق پور، بابك اعرابي، فريد شيخ الاسلام
كد ايرانداك :
ID487 دكتري
چكيده فارسي :
به فارسي و انگليسي: قابل رويت در نسخه ديجيتالي
چكيده انگليسي :
Non Parametric Statistical Inference with Imprecise Information Gholamreza Hesamian g hesamian@math iut ac ir December 26 2012 Doctor of Philosophy Thesis Department of Mathematical Sciences Isfahan University of Technology Isfahan 84156 83111 Iran Supervisor Dr S Mahmoud Taheri taheri@cc iut ac ir Advisor Professor Mashaallah Mashinchi mashinchi@mail uk ac ir Department Graduate Program Coordinator Dr Hamidreza Z Zangeneh Department of Mathematical Sciences Isfahan University of Technology Isfahan 84156 83111 Iran Department of Statistics Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Abstract Four topics of statistical inference are investigated in fuzzy environment I Analysis of a two way contingency table with imprecise information II A generalization of the Wilcoxon signed rank test and its applications III Kolmogorov Smirnov one sample test in totally fuzzy environment and IV Linear rank tests for two sample fuzzy data For the rst topic we propose a procedure to extend the Goodman Kruskal gamma statistic and a method of testing hypothesis of independence in two way contingency tables with fuzzy information For the second topic we extend the classical Wilcoxon signed rank test for imprecise observations when the given signi cance level is also a fuzzy number For the third topic we propose a new method to compute the so called fuzzy p value for testing imprecise one sided hypotheses To do this we extend the concept of fuzzy distribu tion function and fuzzy empirical distribution function at a crisp as well as a fuzzy number Then we state and prove some large sample properties of the fuzzy empirical distribution function Such properties are used to construct a suitable test statistic Finally for evaluat ing the null hypothesis we employ a fuzzy preference to compare the observed fuzzy p value and the given fuzzy signi cance level
استاد راهنما :
محمود طاهري
استاد مشاور :
ماشاءالله ماشين چي
استاد داور :
بهرام صادق پور، بابك اعرابي، فريد شيخ الاسلام
