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A Note on Local Influence Based on Normal Curvature

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  • Wing K. Fung
  • C. W. Kwan

Abstract

Object functions other than the likelihood displacement, such as a parameter estimate or a test statistic, can also be used in local influence analysis. The normal curvatures of these object functions have been studied in situations where the slopes were non‐zero. In these situations, we show that the normal curvature is not scale invariant and thus ambiguous conclusions will be drawn. Comments on the application of the general normal curvature formula are presented.

Suggested Citation

  • Wing K. Fung & C. W. Kwan, 1997. "A Note on Local Influence Based on Normal Curvature," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 839-843.
    • Handle: RePEc:bla:jorssb:v:59:y:1997:i:4:p:839-843
    DOI: 10.1111/1467-9868.00100
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    Citations

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    Cited by:

    1. Manuel Galea & Gilberto Paula & Miguel Uribe-Opazo, 2003. "On influence diagnostic in univariate elliptical linear regression models," Statistical Papers, Springer, vol. 44(1), pages 23-45, January.
    2. Xu-Ping Zhong & Bo-Cheng Wei & Wing-Kam Fung, 2000. "Influence Analysis for Linear Measurement Error Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(2), pages 367-379, June.
    3. Leiva, Victor & Barros, Michelli & Paula, Gilberto A. & Galea, Manuel, 2007. "Influence diagnostics in log-Birnbaum-Saunders regression models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5694-5707, August.
    4. Giménez, Patricia & Galea, Manuel, 2013. "Influence measures on corrected score estimators in functional heteroscedastic measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 1-15.
    5. Nyangoma, S.O. & Fung, W.-K. & Jansen, R.C., 2006. "Identifying influential multinomial observations by perturbation," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2799-2821, June.
    6. Hong Gu & Wing Fung, 2000. "Influence Diagnostics in the Common Canonical Variates Model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(4), pages 753-766, December.
    7. Manuel Galea & Patricia Giménez, 2019. "Local influence diagnostics for the test of mean–variance efficiency and systematic risks in the capital asset pricing model," Statistical Papers, Springer, vol. 60(1), pages 293-312, February.
    8. Andy Lee & John Yick & Yer Van Hui, 2001. "Sensitivity of the portmanteau statistic in time series modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 691-702.
    9. Ortega, Edwin M.M. & Cordeiro, Gauss M. & Lemonte, Artur J., 2012. "A log-linear regression model for the β-Birnbaum–Saunders distribution with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 698-718.
    10. Ortega, Edwin M. M. & Bolfarine, Heleno & Paula, Gilberto A., 2003. "Influence diagnostics in generalized log-gamma regression models," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 165-186, February.
    11. Hong Gu & Wing Fung, 1998. "Assessing Local Influence in Canonical Correlation Analysis," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(4), pages 755-772, December.
    12. Wei, Wen Hsiang, 2004. "Derivatives diagnostics and robustness for smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 46(2), pages 335-356, June.
    13. Xibin Zhang & Maxwell L. King, 2002. "Influence Diagnostics in GARCH Processes," Monash Econometrics and Business Statistics Working Papers 19/02, Monash University, Department of Econometrics and Business Statistics.
    14. Gu, Hong & Fung, Wing K., 2001. "Influence Diagnostics in Common Principal Components Analysis," Journal of Multivariate Analysis, Elsevier, vol. 79(2), pages 275-294, November.
    15. Rasekh, A.R., 2006. "Local influence in measurement error models with ridge estimate," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2822-2834, June.

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