Influence measures in ridge regression when the error terms follow an Ar(1) process

• Published in: Computational statistics Vol. 31; no. 3; pp. 879 - 898
• Main Authors: Sokuet Acar, Tuba, Oezkale, MRevan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2016; Springer Nature B.V
• Subjects: Computer simulation; Deletion; Economic Theory/Quantitative Economics/Mathematical Methods; Error analysis; Errors; Mathematical models; Mathematics; Mathematics and Statistics; Monte Carlo simulation; Original Paper; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Regression; Regression analysis; Ridges; Statistics; Studies; Autocorrelation; Regression diagnostics; Multicollinearity; Generalized least squares estimator; Ridge estimator
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-015-0615-5

Influence concepts have an important place in linear regression models and case deletion is a useful method for assessing the influence of single case. The influence measures in the presence of multicollinearity were discussed under the linear regression models when the errors structure is uncorrelated and homoscedastic. In contrast to other article on this subject, we consider the influence measures in ridge regression with autocorrelated errors. Theoretical results are illustrated with a numerical example and a Monte Carlo simulation is conducted to see the effect autocorrelation coefficient, strength of multicollinearity and sample size on leverage points and influential observations.