پديد آورنده :
عليمرادي، جواد
عنوان :
برآورد حداكثر درستنمايي پارامترهاي مدل اتور گرسيو با نوفه پايدار
مقطع تحصيلي :
كارشناسي ارشد
گرايش تحصيلي :
آمار رياضي
محل تحصيل :
اصفهان: دانشگاه صنعتي اصفهان، دانشكده علوم رياضي
صفحه شمار :
نه، 110ص.: مصور، جدول، نمودار
يادداشت :
ص.ع. به فارسي و انگليسي
استاد راهنما :
صفيه محمودي
استاد مشاور :
سروش عليمرادي
توصيفگر ها :
مدل هاي اتورگرسيو , غير سببي , غير گوسي , توزيع پايدار , بوت استراپ
تاريخ نمايه سازي :
94/2/16
استاد داور :
افشين پرورده، ساره گلي
تاريخ ورود اطلاعات :
1396/09/27
چكيده انگليسي :
Maximum Likelihood Estimation for Autoregressive Parameters with Stable Noise Javad Alimoradi j alimoradi@math iut ac ir 2015 Department of Mathematical Sciences Isfahan University of Technology Isfahan 84156 83111 Iran Supervisor Dr Sa eh Mahmoodi mahmoodi@cc iut ac ir Advisor Dr Soroush Alimoradi salimora@cc iut ac ir 2010 MSC 62M10 62E20 62F10 Keywords Autoregressive models Maximum likelihood estimation Noncausal Stable Bootstrap Abstract Many observed time series processes appear spiky due to the occasional appearance of observa tions particularly large in absolute value Non Gaussian stable distributions which have regularlyvarying or heavy tail probabilities P X x constant x x 0 0 2 are often usedto model these series Processes exhibiting non Gaussian stable behavior have appeared for exam ple in economics and nance signal processing and teletra c engineering Parameter estimationfor causal heavy tailed autoregressive AR processes has already been considered in the literature Davis and Resnick 1986 least squares estimators LS Davis Knight and Liu 1992 and Davis 1996 least absolute deviations LAD and other M estimators Mikosch Gadrich Kl ppelbergand Adler 1995 Whittle estimators Ling 2005 weighted least absolute deviations estimators WLAD The weighted least absolute deviations estimators for causal AR parameters are n1 2 n 1 consistent and the least squares and Whittle estimators are ln n consistent while the unweightedleast absolute deviations estimators have the same rate of convergence as ML estimators n1 The ory of convergence has not yet been developed for the distribution of AR parameter estimators whenthe process is noncausal and heavy tailed In this thesis we focus on maximum likelihood ML estimation for the parameters of both causaland noncausal autoregressive time series processes with non Gaussian stable noise based on articlesby Andrews Calder and Davis 2009 and Breidt and Davis 1991
استاد راهنما :
صفيه محمودي
استاد مشاور :
سروش عليمرادي
استاد داور :
افشين پرورده، ساره گلي
