پديد آورنده :
اصلاني آخوره عليائي، نيلوفر
عنوان :
رگرسيون چندكي بيزي در داده هاي پيوسته داراي انباشتگي در صفر
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
آمار اقتصادي و اجتماعي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده علوم رياضي
صفحه شمار :
پانزده، [۱۰۵]ص.: مصور، جدول، نمودار
استاد راهنما :
زهرا صابري
استاد مشاور :
ريحانه ريخته گران
توصيفگر ها :
توزيع لاپلاس نامتقارن , سانسور چپ , داده هاي نسبت , رگرسيون چندكي بيزي , مدل دوبخشي
استاد داور :
محمد محمدي، مرجان منصوريان
تاريخ ورود اطلاعات :
1396/11/28
چكيده انگليسي :
Bayesian quantile regression analysis for continuous data with a discrete component at zero Niloofar Aslani Akhore Oleiayi n aslani@math iut ac ir 2018 Department of Mathematical Sciences Isfahan University of Technology Isfahan 84156 83111 Iran Supervisor Dr Zahra Saberi z saberi@cc iut ac ir Advisor Dr Reyhaneh Rikhtehgaran r rikhtehgaran@cc iut ac ir 2010 MSC 62J99 62F15 62F10 Keywords Asymmeetric Laplace distribution Bayesian quantile regression Left censoring Pro portion data Two part model Abstract In order to investigate the relationship between one or more explanatory variables and the responsevariable regression methods are used which are very importance The classical theory of linearmodels is essentially a theory for models of conditional expectations In many applications however it is fruitful to go beyond these models Quantile regression is a statistical analysis able to detectmore effects than conventional procedures it does not restrict attention to the conditional mean andtherefore it permits to approximate the whole conditional distribution of a response variable Quantileregression is gradually emerging as a comprehensive approach to the statistical analysis of linear andnonlinear response models Quantile regression supplements the exclusive focus of least squares basedmethods on the estimation of conditional mean functions with a general technique for estimatingfamilies of conditional quantile functions This greatly expands the flexibility of both parametric andnonparametric regression methods In regression analysis it is common to find proportion data as theresponse variable such as the proportion of income reserved for food expenditure or the proportion ofcrude oil converted to gasoline In such cases one can use a transformation of the response variable and follow with a linear regression analysis by considering a normal distribution for the errors but atthe cost of losing parameters interpretation in this process Therefore in order to provide widely usedand useful analyses of the mentioned data quantile regression models are introduced that in addition
استاد راهنما :
زهرا صابري
استاد مشاور :
ريحانه ريخته گران
استاد داور :
محمد محمدي، مرجان منصوريان
