References
- Cui, X., W. Guo, L. Lin, and L. Zhu. 2009. Covariate-adjusted nonlinear regression. The Annals of Statistics 37 (4):1839–70. doi: 10.1214/08-AOS627.
- de Castro, M., and I. Vidal. 2019. Bayesian inference in measurement error models from objective priors for the bivariate normal distribution. Statistical Papers 60 (4):1059–78. doi: 10.1007/s00362-016-0863-7.
- Delaigle, A., P. Hall, and W.-X. Zhou. 2016. Nonparametric covariate-adjusted regression. The Annals of Statistics 44 (5):2190–220. doi: 10.1214/16-AOS1442.
- Kaysen, G. A., J. A. Dubin, H.-G. Müller, W. E. Mitch, L. M. Rosales, and N. W. Levin. 2002. Relationships among inflammation nutrition and physiologic mechanisms establishing albumin levels in hemodialysis patients. Kidney International 61 (6):2240–9. doi: 10.1046/j.1523-1755.2002.00076.x.
- Kim, M., and Y. Ma. 2012. The efficiency of the second-order nonlinear least squares estimator and its extension. Annals of the Institute of Statistical Mathematics 64 (4):751–64. doi: 10.1007/s10463-011-0332-y.
- Li, G., J. Zhang, and S. Feng. 2016. Modern measurement error models. Beijing: Science Press.
- Mack, Y. P., and B. W. Silverman. 1982. Weak and strong uniform consistency of kernel regression estimates. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 61 (3):405–15. doi: 10.1007/BF00539840.
- Nadaraya, E. A. 1964. On estimating regression. Theory of Probability & Its Applications 9 (1):141–2. doi: 10.1137/1109020.
- Şentürk, D., and H.-G. Müller. 2005. Covariate-adjusted regression. Biometrika 92 (1):75–89. doi: 10.1093/biomet/92.1.75.
- Tomaya, L. C., and M. de Castro. 2018. A heteroscedastic measurement error model based on skew and heavy-tailed distributions with known error variances. Journal of Statistical Computation and Simulation 88 (11):2185–200. doi: 10.1080/00949655.2018.1452925.
- Wang, L., and A. Leblanc. 2008. Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics 60 (4):883–900. doi: 10.1007/s10463-007-0139-z.
- Watson, G. S. 1964. Smooth regression analysis, Sankhyā. The Indian Journal of Statistics, Series A 26:359–72.
- Wu, C.-F. 1981. Asymptotic theory of nonlinear least squares estimation. The Annals of Statistics 9 (3):501–13. doi: 10.1214/aos/1176345455.
- Xie, C., and L. Zhu. 2019. A goodness-of-fit test for variable-adjusted models. Computational Statistics & Data Analysis 138:27–48. doi: 10.1016/j.csda.2019.01.018.
- Yang, Y., T. Tong, and G. Li. 2019. Simex estimation for single-index model with covariate measurement error. AStA Advances in Statistical Analysis 103 (1):137–61. doi: 10.1007/s10182-018-0327-6.
- Zhang, J. 2019. Partial linear models with general distortion measurement errors. Electronic Journal of Statistics 13 (2):5360–414. doi: 10.1214/19-EJS1654.
- Zhang, J. 2021. Estimation and variable selection for partial linear single-index distortion measurement errors models. Statistical Papers 62 (2):887–913. doi: 10.1007/s00362-019-01119-6.
- Zhang, J., and X. Cui. 2021. Logarithmic calibration for nonparametric multiplicative distortion measurement errors models. Journal of Statistical Computation and Simulation 91 (13):2623–44. doi: 10.1080/00949655.2021.1904240.
- Zhang, J., and Y. Zhou. 2020. Calibration procedures for linear regression models with multiplicative distortion measurement errors. Brazilian Journal of Probability and Statistics 34 (3):519–36. doi: 10.1214/19-BJPS451.
- Zhang, J., B. Lin, and G. Li. 2019. Nonlinear regression models with general distortion measurement errors. Journal of Statistical Computation and Simulation 89 (8):1482–504. doi: 10.1080/00949655.2019.1586904.
- Zhang, J., B. Lin, and Z. Feng. 2020. Conditional absolute mean calibration for partial linear multiplicative distortion measurement errors models. Computational Statistics & Data Analysis 141:77–93. doi: 10.1016/j.csda.2019.06.009.
- Zhang, J., G. Li, and Y. Yang. 2021. Modal linear regression models with multiplicative distortion measurement errors. Statistical Analysis and Data Mining: The ASA Data Science Journal. Advance online publication. doi: 10.1002/sam.11541.
- Zhang, J., G. Li, and Z. Feng. 2015. Checking the adequacy for a distortion errors-in-variables parametric regression model. Computational Statistics & Data Analysis 83:52–64. doi: 10.1016/j.csda.2014.09.018.
- Zhang, J., L. Zhu, and H. Liang. 2012. Nonlinear models with measurement errors subject to single-indexed distortion. Journal of Multivariate Analysis 112:1–23. doi: 10.1016/j.jmva.2012.05.012.
- Zhang, J., Y. Gai, X. Cui, and G. Li. 2020. Measuring symmetry and asymmetry of multiplicative distortion measurement errors data. Brazilian Journal of Probability and Statistics 34 (2):370–93. doi: 10.1214/19-BJPS432.
- Zhang, J., Y. Yang, and G. Li. 2020. Logarithmic calibration for multiplicative distortion measurement errors regression models. Statistica Neerlandica 74 (4):462–88. doi: 10.1111/stan.12204.
- Zhang, J., Y. Yang, S. Feng, and Z. Wei. 2020. Logarithmic calibration for partial linear models with multiplicative distortion measurement errors. Journal of Statistical Computation and Simulation 90 (10):1875–96. doi: 10.1080/00949655.2020.1750614.
- Zhang, J., Z. Xu, and Z. Wei. 2020. Absolute logarithmic calibration for correlation coefficient with multiplicative distortion. Communications in Statistics - Simulation and Computation. Advance online publication. doi: 10.1080/03610918.2020.1859541.
- Zhao, J., and C. Xie. 2018. A nonparametric test for covariate-adjusted models. Statistics & Probability Letters 133 (Supplement C):65–70. doi: 10.1016/j.spl.2017.10.004.