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Effects of measurement error on a class of single-index varying coefficient regression models

Author

Listed:
  • Jianhong Shi

    (Shanxi Normal University)

  • Qian Yang

    (Shanxi Normal University)

  • Xiongya Li

    (Kansas State University)

  • Weixing Song

    (Kansas State University)

Abstract

This paper investigates the estimation in a class of single-index varying coefficient regression model when some covariates are contaminated with measurement errors. A bias-corrected least square procedure based on the observed data is proposed. By replacing the nonparametric single index part with a local linear approximation, an iterative algorithm for estimating the index parameter is proposed. More importantly, a special case is identified in which the naive procedure provides consistent estimates for the single index parameters. Large sample properties of the proposed estimators are established. The finite sample performance of the proposed estimators are evaluated by simulation studies.

Suggested Citation

  • Jianhong Shi & Qian Yang & Xiongya Li & Weixing Song, 2017. "Effects of measurement error on a class of single-index varying coefficient regression models," Computational Statistics, Springer, vol. 32(3), pages 977-1001, September.
  • Handle: RePEc:spr:compst:v:32:y:2017:i:3:d:10.1007_s00180-017-0726-2
    DOI: 10.1007/s00180-017-0726-2
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    References listed on IDEAS

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    1. Lixing Zhu & Liugen Xue, 2006. "Empirical likelihood confidence regions in a partially linear single‐index model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 549-570, June.
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    6. Xue, Liu-Gen & Zhu, Lixing, 2006. "Empirical likelihood for single-index models," Journal of Multivariate Analysis, Elsevier, vol. 97(6), pages 1295-1312, July.
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