New influence diagnostics in ridge regression

Emami, Hadi; Emami, Mostafa

doi:10.1080/02664763.2015.1070804

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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

Amin

Faisal

Akram

2021

Journal of Chemometrics

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“…Application of the conventional influence measures in the RR and LE of ordinary linear regression has seen a great surge of research activities during the last two decades or so. It is clear from several articles that appeared in the lit-erature; see, e.g., Walker and Brich (1988), Asar and Erisoglu (2016) and Emami and Emami (2015). However, the LE is generalized to fit the semiparametric regression model for multicollinearity data (see Akdeniz and Akdeniz Duran (2010), Duran et al ( 2012) and Duran et al (2011)).…”

Section: Introductionmentioning

confidence: 99%

Case Deletion Diagnostics in Liu Semiparametric Regression Models

Emami¹

2021

Journal of Data Science

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In semiparametric regression it is of interest to detect anomalous observations that exert an unduly large influence on the parameter's esti-mate and fitted values. Usually the existence of influential observations is complicated by the presence of collinearity. However no method of influ-ence diagnostics available for the possible effects that collinearity can have on the influence of an observation on the estimates of parametric and non-parametric component of semiparametric regression models. In this paper we show when Liu estimators are used to mitigate the effects of collinearity the influence of some observations can be drastically modified. We propose a case deletion formula to detect influential points in Liu estimators of semi-parametric regression models . As an illustrative example a real data set are analysed.

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