Influence diagnostics in the inverse Gaussian ridge regression model: Applications in chemometrics. (30th March 2021)

• Record Type: Journal Article
• Authors: Amin, Muhammad; Faisal, Muhammad; Akram, Muhammad Nauman
• Is Part Of: Journal of chemometrics. Volume 35:Number 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-30
• Subjects: IGRM -- IGRRM -- influential observation -- multicollinearity -- ridge estimator; Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.3342
• Languages: English
• ISSNs: 0886-9383

Abstract

Abstract: The influential observation affects the regression model inferences. Literature has shown that the problems of multicollinearity and influential observations can jointly exist in a model. The ridge regression estimator has been developed to handle the challenge of multicollinearity. The detection of influential observations with multicollinearity and its impact on the ridge estimates is necessary for better decision making. In this article, we proposed some influence diagnostics for the inverse Gaussian ridge regression model (IGRRM). The proposed diagnostics are evaluated with the help of a simulation study and two chemometric‐related data sets. We found that the covariance ratio (CVR) method is better than other methods for the detection of influential observations under smaller dispersion. While for larger dispersion, all the IGRRM diagnostics perform equally well for the identification of influential observations. Abstract : This study focuses on the proposal of influence diagnostic methods for the inverse Gaussian ridge regression model. Monte Carlo simulation study and two chemometric data sets are considered for the evaluation of proposed influence diagnostic methods. Results show that the covariance ratio method gives the better performance as compared with the other methods under smaller dispersion.