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Statistical Inference for Estimators in a Semiparametric EV Model with Linear Process Errors and Missing Responses

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  • Jing-Jing Zhang
  • Xue Yang
  • Jelena Nikolić

Abstract

This paper concentrates on the properties of estimators in a semiparametric EV model, particularly considering the effects of missing data and linear process errors according to the actual situation. The missing data are processed by three different methods: the direct deletion method, imputation (interpolation fill) method, and regression surrogate method. Also, the corresponding estimators of the slope parameter β and the nonparameter variable g⋅ are obtained. All the estimators are asymptotically normal, and the consistency rates for which can achieve on−1/6 log n. Besides, the performance of the estimators is investigated by one sample experiment.

Suggested Citation

  • Jing-Jing Zhang & Xue Yang & Jelena Nikolić, 2023. "Statistical Inference for Estimators in a Semiparametric EV Model with Linear Process Errors and Missing Responses," Mathematical Problems in Engineering, Hindawi, vol. 2023, pages 1-23, April.
    • Handle: RePEc:hin:jnlmpe:2547329
    DOI: 10.1155/2023/2547329
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