عنوان :
خوشه بندي داده هاي طولي با استفاده از آميخته ي فرايند ديريكله
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
آمار رياضي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده علوم رياضي
صفحه شمار :
يازده، 105ص.: نمودار
استاد راهنما :
ريحانه ريخته گران
استاد مشاور :
مريم كلكين نما
توصيفگر ها :
روش هاي شبيه سازي مونت كارلوي زنجير ماركفي , مدل با اثرات آميخته پويا
استاد داور :
ايرج كاظمي، سروش عليمرادي
تاريخ ورود اطلاعات :
1395/01/14
چكيده انگليسي :
Clustering of longitudinal data using Dirichlet process mixture Elaheh Shams e shams@math iut ac ir 2016 Department of Mathematical Sciences Isfahan University of Technology Isfahan 84156 83111 Iran Supervisor Dr Reyhaneh Rikhtehgaran r rikhtehgaran@cc iut ac ir Advisor Dr Maryam Kelkinnama m kelkinnama@cc iut ac ir 4 2010 MSC 62J99 62 07 5 Keywords Dirichlet process Dynamic mixed e ects model longitodinal data Markov chainMonte Carlo simulation methods AbstractCluster analysis is the task of grouping a set of subjects in such a way that according to some speci ccriteria subjects in the same group called a cluster are more similar to each other than to those inother clusters Clustering of longitudinal data which is provided by repeatedly measuring subjectsover time is utilized to recognize data patterns Frequently used similarity criteria are mostly basedon distance measures and cannot easily be extended to cluster longitudinal data In this thesis weuse a model based clustering of longitudinal data based on mixture models whose similarity criterionis supported by having the same distribution In this way the most information of data is transmittedto the clustering By considering this viewpoint a wide range of applications is available in a lot of elds like medicine public health education business economics psychology biology and more In this thesis to consider of a wide variety of correlation patterns in longitudinal data and also toutilize explanatory variables in clustering we use dynamic mixed e ects models These models cancontrol the between subjects variation and cover the serial correlation among observations respec tively by entering random e ects and lagged response variables to the model Also we handle theissue of the initial conditions that is appeared in tting dynamic mixed e ects models This is doneby emphasizing on the joint modeling of start up and subsequent responses
استاد راهنما :
ريحانه ريخته گران
استاد مشاور :
مريم كلكين نما
استاد داور :
ايرج كاظمي، سروش عليمرادي
