Influence diagnostic in ridge semiparametric models

Emami, Hadi

doi:10.1016/j.spl.2015.06.010

Cited by 7 publications

(1 citation statement)

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“…There does not seem to have any work on diagnostics for semiparametric regression models in present of collinearity. Recently, Emami (2015) and Emami (2016) developed influence diagnostics based case deletion and local influence approach for ridge estima-tors and Liu estimators in semiparametric regression models, respectively. In this paper, therefore, we propose a case deletion formula to detect influential points in LE for semiparametric models.…”

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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Section: Introductionmentioning

confidence: 99%