Statistical inference for linear regression models with additive distortion measurement errors
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DOI: 10.1007/s00362-018-1057-2
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References listed on IDEAS
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Cited by:
- Da Chen & Linlin Dai & Yichuan Zhao, 2024. "Jackknife empirical likelihood for the correlation coefficient with additive distortion measurement errors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(4), pages 1129-1159, December.
- Jun Zhang & Bingqing Lin & Yan Zhou, 2024. "Linear regression models with multiplicative distortions under new identifiability conditions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(1), pages 25-67, February.
- Jun Zhang, 2021. "Model checking for multiplicative linear regression models with mixed estimators," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(3), pages 364-403, August.
- Jingxuan Luo & Lili Yue & Gaorong Li, 2023. "Overview of High-Dimensional Measurement Error Regression Models," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
- Huilan Liu & Xiawei Zhang & Huaiqing Hu & Junjie Ma, 2024. "Analysis of the positive response data with the varying coefficient partially nonlinear multiplicative model," Statistical Papers, Springer, vol. 65(5), pages 3063-3092, July.
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Keywords
Measurement errors; Kernel smoothing; Linear regression models; Residual based estimator; Model Checking;All these keywords.
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