Pena's statistic for the Liu regression

Kashif, Muhammad; Amanullah, M. Mohamed; Aslam, Muhammad

doi:10.1080/00949655.2018.1468444

Cited by 5 publications

(2 citation statements)

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“…The detection percentage of the included influential observation is considered as the performance evaluation criteria of the proposed methods. 40,41 This assessment criteria is defined as Detection % ð Þ= Total number of detections in R replications Total number of replication × 100, where R represents the number of replications which are set to be 1000. The simulation study under various parametric conditions is completed with the help of R software.…”

Section: Monte Carlo Simulation Studymentioning

confidence: 99%

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Influence diagnostics in the inverse Gaussian ridge regression model: Applications in chemometrics

Amin

Faisal

Akram

2021

Journal of Chemometrics

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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.

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Section: Monte Carlo Simulation Studymentioning

confidence: 99%

“…32,33 This issue has been well studied for the LMs. [32][33][34][35][36][37][38][39][40][41] Controlling the multicollinearity may increase the effect of influence cases. 33,38,39 Belsley et al 33 pointed out that the performance of influence diagnostics maybe change using the ridge estimation approach.…”

Section: Introductionmentioning

confidence: 99%