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Detecting influential observations in Liu and modified Liu estimators

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  • Hasan Ertas
  • Murat Erisoglu
  • Selahattin Kaciranlar

Abstract

In regression, detecting anomalous observations is a significant step for model-building process. Various influence measures based on different motivational arguments are designed to measure the influence of observations through different aspects of various regression models. The presence of influential observations in the data is complicated by the existence of multicollinearity. The purpose of this paper is to assess the influence of observations in the Liu [9] and modified Liu [15] estimators by using the method of approximate case deletion formulas suggested by Walker and Birch [14]. A numerical example using a real data set used by Longley [10] and a Monte Carlo simulation are given to illustrate the theoretical results.

Suggested Citation

  • Hasan Ertas & Murat Erisoglu & Selahattin Kaciranlar, 2013. "Detecting influential observations in Liu and modified Liu estimators," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1735-1745, August.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:8:p:1735-1745
    DOI: 10.1080/02664763.2013.794203
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    Cited by:

    1. Hadi Emami & Mostafa Emami, 2016. "New influence diagnostics in ridge regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 476-489, March.

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