Abstract
Outliers and influential observations have important effects on the regression analysis. The goal of this paper is to extend the mean-shift model for detecting outliers in case of ridge regression model in the presence of stochastic linear restrictions when the error terms follow by an autoregressive AR(1) process. Furthermore, extensions of measures for diagnosing influential observations are derived. A numerical example of a real data set is used to illustrate the findings. Finally, a simulation study is conducted to evaluate the performance of the proposed procedure and measures. Results of this study show the efficiency of the proposed mean-shift outlier model for the proposed model. Also, the study resulted in some findings about the behavior of suggested measures for the specified model. In fact, these measures are affected by the degree of collinearity and the size of autocorrelation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.