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Influence measures on corrected score estimators in functional heteroscedastic measurement error models

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  • Giménez, Patricia
  • Galea, Manuel

Abstract

This paper deals with the local influence assessment of the effects of minor perturbations of data on corrected score estimators in the functional heteroscedastic measurement error models with known variances. By extending to the context of measurement error models the differential-geometrical framework proposed by Zhu et al. [H.T. Zhu, J.G. Ibrahim, S. Lee, H. Zhang, Perturbation selection and influence measures in local influence analysis, The Annals of Statistics 35 (2007) 2565–2588], an n-dimensional Riemannian manifold is defined. The associated metric tensor is utilized for the selection of appropriate perturbation schemes. The Levi-Civita connection and first and second derivatives of the corrected score estimators are used to construct influence measures. Simple formulas are obtained under different perturbation schemes. A comparison with the slope and curvature based diagnostics defined from the surface of the corrected score estimators formed by perturbation is included. A real data application and a simulated example illustrate the performance of the proposed diagnostics.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:114:y:2013:i:c:p:1-15
    DOI: 10.1016/j.jmva.2012.07.002
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    References listed on IDEAS

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    1. 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.
    2. de Castro, Mario & Galea-Rojas, Manuel & Bolfarine, Heleno, 2007. "Local influence assessment in heteroscedastic measurement error models," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1132-1142, October.
    3. Hong‐Tu Zhu & Sik‐Yum Lee, 2001. "Local influence for incomplete data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 111-126.
    4. Xiaoyan Shi & Hongtu Zhu & Joseph G. Ibrahim, 2009. "Local Influence for Generalized Linear Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 65(4), pages 1164-1174, December.
    5. W.‐Y. Poon & Y. S. Poon, 1999. "Conformal normal curvature and assessment of local influence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 51-61.
    6. Giménez, Patricia & Patat, María Laura, 2005. "Estimation in comparative calibration models with replicate measurement," Statistics & Probability Letters, Elsevier, vol. 71(2), pages 155-164, February.
    7. 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.
    8. Lee, Sik-Yum & Xu, Liang, 2004. "Influence analyses of nonlinear mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 321-341, March.
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    Cited by:

    1. Riquelme, Marco & Bolfarine, Heleno & Galea, Manuel, 2015. "Robust linear functional mixed models," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 82-98.
    2. 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.

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