Doğrusal regreson modellerinde uç değerlerin ve etkin gözlemlerin belirlenmesinde bootstrapten-sonra jackknife yöntemi

In this thesis, the jackknife-after-bootstrap method which was proposed by Bradley Efron (1992) for estimating the standard errors and bias of a statistic, and proposed by Martin and Roberts in the context of influence diagnostics have been investigated. In addition, this method extended for several influence measures such as t-star statistic, Likelihood Distance, Welsch' Distance and Modified Cook's Distance. Moreover, the therminology and algorithm of this method have been studied in detail. The studies were supported with several simulation studies and real-world examples, and the results were compared with traditional results. These simulation programs have been run by using R 2.14.0. Also in this study, the sufficient bootstrap have been studied, and it was applied with jackknife-after-bootstrap algorithm. We call this method as sufficient jackknife-after-bootstrap method. The same simulation studies and real-world examples have been carried out for this method, and the results were compared with conventional jackknife-after-bootstrap results.