Enhancement of partial robust M-regression (PRM) performance using Bisquare weight function

• Published in: AIP Conference Proceedings
• Main Author: Mohamad M.; Ramli N.M.; Mamat N.A.M.; Ahmad S.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics Inc. 2014
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010868513&doi=10.1063%2f1.4894338&partnerID=40&md5=cac4c0bef273110a6e6a846df8637ee9

Partial Least Squares (PLS) regression is a popular regression technique for handling multicollinearity in low and high dimensional data which fits a linear relationship between sets of explanatory and response variables. Several robust PLS methods are proposed to accommodate the classical PLS algorithms which are easily affected with the presence of outliers. The recent one was called partial robust M-regression (PRM). Unfortunately, the use of monotonous weighting function in the PRM algorithm fails to assign appropriate and proper weights to large outliers according to their severity. Thus, in this paper, a modified partial robust M-regression is introduced to enhance the performance of the original PRM. A re-descending weight function, known as Bisquare weight function is recommended to replace the fair function in the PRM. A simulation study is done to assess the performance of the modified PRM and its efficiency is also tested in both contaminated and uncontaminated simulated data under various percentages of outliers, sample sizes and number of predictors. © 2014 AIP Publishing LLC.