References
- Fuller WA. Measurement error models. New York: Wiley; 1987. (Wiley Series in Probability and Mathematical Statistics: Probability and Mathematical Statistics).
- Carroll RJ, Ruppert D, Stefanski LA, et al. Nonlinear measurement error models, a modern perspective. 2nd ed. New York: Chapman and Hall; 2006.
- Hellton KH, Thoresen M. The impact of measurement error on principal component analysis. Scand Stat Theory Appl. 2014;41(4):1051–1063.
- Li G, Zhang J, Feng S. Modern measurement error models. Beijing: Science Press; 2016.
- Liang H. Generalized partially linear mixed-effects models incorporating mismeasured covariates. Ann Inst Stat Math. 2009;61(1):27–46.
- Liang H, Härdle W, Carroll RJ. Estimation in a semiparametric partially linear errors-in-variables model. Ann Stat. 1999;27(5):1519–1535.
- Liang H, Ren H. Generalized partially linear measurement error models. J Comput Graph Stat. 2005;14(1):237–250.
- Wang HY, Chen X, Flournoy N. The focused information criterion for varying-coefficient partially linear measurement error models. Stat Papers. 2016;57(1):99–113.
- Wang M, Liu C, Xie T, et al. Data-driven model checking for errors-in-variables varying-coefficient models with replicate measurements. Comput Stat Data Anal. 2020;141:12–27.
- Wang M, Zhao P, Kang X. Structure identification for varying coefficient models with measurement errors based on kernel smoothing. Stat Papers. 2020;61:1841–1857.
- Yang Y, Tong T, Li G. Simex estimation for single-index model with covariate measurement error. AStA Adv Stat Anal. 2019;103(1):137–161.
- Zheng Z, Li Y, Yu C, et al. Balanced estimation for high-dimensional measurement error models. Comput Stat Data Anal. 2018;126:78–91.
- Kaysen GA, Dubin JA, Müller H-G, et al. Relationships among inflammation nutrition and physiologic mechanisms establishing albumin levels in hemodialysis patients. Kidney Int. 2002;61:2240–2249.
- entürk D, Müller H-G. Covariate adjusted correlation analysis via varying coefficient models. Scand Stat Theory Appl. 2005;32(3):365–383.
- entürk D, Müller H-G. Inference for covariate adjusted regression via varying coefficient models. Ann Stat. 2006;34:654–679.
- Cui X, Guo W, Lin L, et al. Covariate-adjusted nonlinear regression. Ann Stat. 2009;37:1839–1870.
- Delaigle A, Hall P, Zhou W-X. Nonparametric covariate-adjusted regression. Ann Stat. 2016;44(5):2190–2220.
- Li F, Lin L, Cui X. Covariate-adjusted partially linear regression models. Commun Stat Theor Meth. 2010;39(6):1054–1074.
- Li F, Lin L, Lu Y, et al. An adaptive estimation for covariate-adjusted nonparametric regression model. Stat Papers. 2021;62:93–115. DOI:10.1007/s00362-019-01084-0
- Li F, Lu Y. Lasso-type estimation for covariate-adjusted linear model. J Appl Stat. 2018;45(1):26–42.
- Nguyen DV, entürk D. Multicovariate-adjusted regression models. J Stat Comput Simul. 2008;78:813–827.
- entürk D, Nguyen DV. Asymptotic properties of covariate-adjusted regression with correlated errors. Stat Probab Lett. 2009;79:1175–1180.
- Xie C, Zhu L. A goodness-of-fit test for variable-adjusted models. Comput Stat Data Anal. 2019;138:27–48.
- Zhang J. Estimation and variable selection for partial linear single-index distortion measurement errors models. Stat Papers. 2021;62:887–913.
- Zhang J, Zhu J, Zhou Y, et al. Multiplicative regression models with distortion measurement errors. Stat Papers. 2020;61:2031–2057.
- Zhao J, Xie C. A nonparametric test for covariate-adjusted models. Stat Probab Lett. 2018;133:65–70.
- Belomestny D, Comte F, Genoncatalot V. Nonparametric laguerre estimation in the multiplicative censoring model. Electron J Stat. 2016;10(2):3114–3152.
- Brunel E, Comte F, Genoncatalot V. Nonparametric density and survival function estimation in the multiplicative censoring model. Test. 2016;25(3):570–590.
- Hwang JT. Multiplicative errors-in-variables models with applications to recent data released by the U.S. Department of Energy. J Am Stat Assoc. 1986;81(395):680–688.
- Zhang J, Yang Y, Feng S, et al. Logarithmic calibration for partial linear models with multiplicative distortion measurement errors. J Stat Comput Simul. 2020;90(10):1875–1896.
- Zhang J, Yang Y, Li G. Logarithmic calibration for multiplicative distortion measurement errors regression models. Stat Neerl. 2020;74:462–488. DOI:10.1111/stan.12204
- Fan J, Gijbels I. Local polynomial modelling and its applications. London: Chapman & Hall; 1996.
- Feng Z, Zhang J, Chen Q. Statistical inference for linear regression models with additive distortion measurement errors. Stat Papers. 2020;61:2483–2509. DOI:10.1007/s00362-018-1057-2
- Feng Z, Gai Y, Zhang J. Correlation curve estimation for multiplicative distortion measurement errors data. J Nonparametr Stat. 2019;31:435–450.
- Liang H, Qin Y, Zhang X, et al. Empirical likelihood-based inferences for generalized partially linear models. Scand Stat Theory Appl. 2009;36(3):433–443.
- Tomaya LC, de Castro M. A heteroscedastic measurement error model based on skew and heavy-tailed distributions with known error variances. J Stat Comput Simul. 2018;88:2185–2200.
- Zhang J, Zhou Y. Calibration procedures for linear regression models with multiplicative distortion measurement errors. Brazilian J Probab Stat. 2020;34(3):519–536.
- Doksum K, Blyth S, Bradlow E, et al. Correlation curves as local measures of variance explained by regression. J Am Stat Assoc. 1994;89:571–582.
- Bjerve S, Doksum KA. Correlation curves: measures of association as functions of covariate values. Ann Stat. 1993;21(2):890–902.
- Silverman BW. Density estimation for statistics and data analysis. Monographs on statistics and applied probability. London: Chapman & Hall; 1986.
- Wand MP, Jones MC. Kernel smoothing. Monographs on statistics and applied probability. Vol. 60. London: Chapman and Hall; 1995.
- Rosman JB, Meijer S, Piers-Becht TPM, et al. Prospective randomized trial of early protein restriction in chronic renal failure. Lancet. 1984;2:1291–1296.