Date of Award

12-2002

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Dr. Joseph W. McKean

Second Advisor

Dr. Joshua D. Naranjo

Third Advisor

Dr. Daniel Mihalko

Fourth Advisor

Dr. Michael Stoline

Abstract

One of the goals of model diagnostics is outlier detection. In particular, we would like to use the residuals, appropriately standardized, to “flag” outliers. Hopefully, our (robust) procedure has yielded a fit that resists undue influence by outlying points, while simultaneously drawing attention to these interesting points via residual analysis. In this study we consider several different methods of standardizing the residuals resulting from autoregression. A large sample approximation for the variance of rank-based first order autoregressive time series residuals is developed. This provides studentized residuals, specific to the time series model and estimation procedure. Simulation studies are presented that illustrate outlier detection ability among different standardization methods, and differences in fits among estimation procedures in the presence of innovation and additive outliers.

Comments

Fifth Advisor: Dr. Bradley Huitema

Access Setting

Dissertation-Open Access

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