Affiliation:
1. University of Nigeria, Nsukka, Nigeria
Abstract
A bewildering large number of test statistics have been found for testing the presence of an outlier in multiple linear regression models. Exact critical values of these test statistics are not available, and approximate ones are usually obtained by the first-order Bonferroni upper bound or large-scale simulations. In this paper, we show that the upper bound values of two of these test statistics are algebraically the same. An application to real data for multiple linear regression is used to demonstrate the procedure.
Reference15 articles.
1. Study on Statistical Outlier Detection and Labelling
2. Identification of Outliers
3. Robust Regression and Outlier Detection
4. Outlier detection in simple linear regression models and robust regression-a case study of wheat production data;A. Rajarathinam;Statistics,2014