Residuals And Influence In Nonlinear Regression For Repeated Measurement Data

All observations don't have equal significance in regression analysis.Diagnostics of observations is an important aspect of model building.In this dissertation,we use diagnostics method to detect residuals and influential points in nonlinear regression for repeated measurement data.Cook distance,likelihood distance and Gauss newton method have been proposed to identify the outliers in nonlinear regression analysis and parameter estimation.Most of these techniques based on graphical representations of residuals,hat matrix and case deletion measures.The results show us detection of single and multiple outliers cases in repeated measurement data by Cook.Distance and Likelihood distance.We use these techniques to explore performance of residuals and influence in nonlinear regression model.