پديد آورنده :
ثابت راسخ، مريم
عنوان :
رويكرد بيزي مدل سازي داده هاي گم شده در داده هاي شمارشي طولي با استفاده از مدل با اثرات آميخته
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
آمار رياضي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده علوم رياضي
صفحه شمار :
دوازده،[90]ص.: مصور،جدول،نمودار
يادداشت :
ص.ع.به فارسي و انگليسي
استاد راهنما :
زهرا صابري
استاد مشاور :
ريحانه ريخته گران
توصيفگر ها :
انصراف آگاهي بخش , مدل گزينش با اثرات آميخته , داده هاي شمارشي طولي , بيش پراكنش
استاد داور :
ايرج كاظمي، ساره گلي
تاريخ ورود اطلاعات :
1395/08/03
چكيده انگليسي :
A Bayesian approach to modelling missing data in longitudinal count data using mixed effects model Maryam Sabetrasekh m sabetrasekh@math iut ac ir 2016 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 62J02 Keywords Missingness Informative drop out Mixed effects selection model Longitudinal count data DICO Overdispersion AbstractLongitudinal data are very common in clinical researches and other field where measurments for a sub ject are collected over time Missing data are unavoidable for longitudinal studies because completefollow up data are often not available for all subjects Different mechanisms for describing missing ness are introduced Mechanism of missingness is said to be missing completely at random MCAR if missing process is independent of both unobserved and observed data missing at random MAR if conditional on the observed data the missing process is independent of the unobserved data andmissing not at random MNAR when the missing process depends only upon the unobserved data Also in longitudinal data missing data can be categorized into two different patterns general inter mittent missing pattern and monoton dropout pattern If mechanism and pattern of missingnessis MNAR and monoton respectively missingness is called informative dropout Analysing such datarequires the more compplex models which incorporate the dropout mechanism in the analysis becausenot considering dropout mechanism into the model lead to invalid estimation for the parameters Inthis situation an indicator variable that describe response variable is observed or not is recorded This indicator variable shows the mechanism of missingness and take 1 if corresponding responsevariable is observed and 0 in another In modelling missing data with respect to joint distribution of
استاد راهنما :
زهرا صابري
استاد مشاور :
ريحانه ريخته گران
استاد داور :
ايرج كاظمي، ساره گلي
