[1] 
Mittag H J. Estimating parameters in a simple errorsinvariables model: A new approach based on finite sample distribution theory. Statistical Papers, 1989, 30: 133140.

[2] 
Fuller W A. Measurement Error Models. New York: Wiley, 1987.

[3] 
Liu J X, Chen X R. Consistency of LS estimator in simple linear EV regression models. Acta Mathematica Scientia, Series B, 2005, 25(1): 5058.

[4] 
Miao Y, Yang G Y, Shen L M. The central limit theorem for LS estimator in simple linear EV regression models.Communications in StatisticsTheory and Methods, 2007, 36(12): 22632272.

[5] 
Miao Y, Yang G Y. The loglog law for LS estimator in simple linear EV regression models. Statistics, 2011, 45(2): 155162.

[6] 
Miao Y, Wang K, Zhao F F. Some limit behaviors for the LS estimator in simple linear EV regression models. Statistics and Probability Letters, 2011, 81(1): 92102.

[7] 
Fan G L, Liang H Y, Wang J F, et al. Asymptotic properties for LS estimators in EV regression model with dependent errors. AStA Advances in Statistical Analysis, 2010, 94: 89103.

[8] 
Yang Q L. Asymptotic normality of LS estimators in the simple linear EV regression model with PA errors. Communications in StatisticsTheory and Methods, 2012, 41(23): 42764284.

[9] 
Miao Y, Zhao F F, Wang K, et al. Asymptotic normality and strong consistency of LS estimators in the EV regression model with NA errors. Statistical Papers, 2013, 54: 193206.

[10] 
Wang X J, Shen A T, Chen Z Y, et al. Complete convergence for weighted sums of NSD random variables and its application in the EV regression model. Test, 2015, 24: 166184.

[11] 
Wang X J, Wu Y, Hu S H. Strong and weak consistency of LS estimators in the EV regression model with negatively superadditivedependent errors. AStA Advances in Statistical Analysis, 2018, 102: 4165.

[12] 
Wang X J, Xi M M, Wang H X, et al. On consistency of LS estimators in the errorsinvariable regression model. Probability in the Engineering and Informational Sciences, 2018, 32: 144162.

[13] 
Shen A T. Asymptotic properties of LS estimators in the errorsinvariables model with MD errors. Statistical Papers, 2019, 60(4): 11931206.

[14] 
Dobrushin R L. The central limit theorem for nonstationary Markov chain. Theory of Probability and Its Applications, 1956, 1: 7288.

[15] 
Babu G J, Ghosh M, Singh K. On rates of convergence to normality for φmixing processes. Sankhya, Series A, 1978, 40(3): 278293.

[16] 
Utev S A. The central limit theorem for φmixing arrays of random variables. Theory of Probability and Its Applications, 1990, 35(1): 131139.

[17] 
Kiesel R. Strong laws and summability for φmixing sequences of random variables. Journal of Theoretical Probability, 1998, 11(1): 209224.

[18] 
Hu S H, Wang X J. Large deviations for some dependent sequences. Acta Mathematica Scientic, Series B, 2008, 28(2): 295300.

[19] 
Yang W Z, Wang X J, Li X Q, et al. BerryEsseen bound of sample quantiles for φmixing random variables. Journal of Mathematical Analysis and Applications, 2012, 388: 451462.

[20] 
Shen A T, Wang X H, Ling J M. On complete convergence for nonstationary φmixing random variables. Communications in StatisticsTheory and Methods, 2014, 43(22): 48564866.

[21] 
Billingsley P. Convergence of Probability Measures. New York : Wiley, 1968.

[22] 
Lu C R, Lin Z Y. Limit Theory for Mixing Dependent Sequences. Beijing: Science Press of China, 1997.

[23] 
Peligrad M, Utev S. Central limit theorem for linear processes. The Annals of Probability, 1997, 25(1): 443456.

[24] 
Thanh Lv, Yin G. Weighted sums of strongly mixing random variables with an application to nonparametric regression. Statistics and Probability Letters, 2015, 105: 195202.

[25] 
Wang X J, Hu S H. Some BaumKatz type results for φmixing random variables with different distributions. RACSAM, 2012, 106: 321331.

[26] 
Wu Q Y. Further study strong consistency of M estimator in linear model for ρmixing random samples. Journal of Systems Science and Complexity, 2011, 24: 969980.

[1] 
Mittag H J. Estimating parameters in a simple errorsinvariables model: A new approach based on finite sample distribution theory. Statistical Papers, 1989, 30: 133140.

[2] 
Fuller W A. Measurement Error Models. New York: Wiley, 1987.

[3] 
Liu J X, Chen X R. Consistency of LS estimator in simple linear EV regression models. Acta Mathematica Scientia, Series B, 2005, 25(1): 5058.

[4] 
Miao Y, Yang G Y, Shen L M. The central limit theorem for LS estimator in simple linear EV regression models.Communications in StatisticsTheory and Methods, 2007, 36(12): 22632272.

[5] 
Miao Y, Yang G Y. The loglog law for LS estimator in simple linear EV regression models. Statistics, 2011, 45(2): 155162.

[6] 
Miao Y, Wang K, Zhao F F. Some limit behaviors for the LS estimator in simple linear EV regression models. Statistics and Probability Letters, 2011, 81(1): 92102.

[7] 
Fan G L, Liang H Y, Wang J F, et al. Asymptotic properties for LS estimators in EV regression model with dependent errors. AStA Advances in Statistical Analysis, 2010, 94: 89103.

[8] 
Yang Q L. Asymptotic normality of LS estimators in the simple linear EV regression model with PA errors. Communications in StatisticsTheory and Methods, 2012, 41(23): 42764284.

[9] 
Miao Y, Zhao F F, Wang K, et al. Asymptotic normality and strong consistency of LS estimators in the EV regression model with NA errors. Statistical Papers, 2013, 54: 193206.

[10] 
Wang X J, Shen A T, Chen Z Y, et al. Complete convergence for weighted sums of NSD random variables and its application in the EV regression model. Test, 2015, 24: 166184.

[11] 
Wang X J, Wu Y, Hu S H. Strong and weak consistency of LS estimators in the EV regression model with negatively superadditivedependent errors. AStA Advances in Statistical Analysis, 2018, 102: 4165.

[12] 
Wang X J, Xi M M, Wang H X, et al. On consistency of LS estimators in the errorsinvariable regression model. Probability in the Engineering and Informational Sciences, 2018, 32: 144162.

[13] 
Shen A T. Asymptotic properties of LS estimators in the errorsinvariables model with MD errors. Statistical Papers, 2019, 60(4): 11931206.

[14] 
Dobrushin R L. The central limit theorem for nonstationary Markov chain. Theory of Probability and Its Applications, 1956, 1: 7288.

[15] 
Babu G J, Ghosh M, Singh K. On rates of convergence to normality for φmixing processes. Sankhya, Series A, 1978, 40(3): 278293.

[16] 
Utev S A. The central limit theorem for φmixing arrays of random variables. Theory of Probability and Its Applications, 1990, 35(1): 131139.

[17] 
Kiesel R. Strong laws and summability for φmixing sequences of random variables. Journal of Theoretical Probability, 1998, 11(1): 209224.

[18] 
Hu S H, Wang X J. Large deviations for some dependent sequences. Acta Mathematica Scientic, Series B, 2008, 28(2): 295300.

[19] 
Yang W Z, Wang X J, Li X Q, et al. BerryEsseen bound of sample quantiles for φmixing random variables. Journal of Mathematical Analysis and Applications, 2012, 388: 451462.

[20] 
Shen A T, Wang X H, Ling J M. On complete convergence for nonstationary φmixing random variables. Communications in StatisticsTheory and Methods, 2014, 43(22): 48564866.

[21] 
Billingsley P. Convergence of Probability Measures. New York : Wiley, 1968.

[22] 
Lu C R, Lin Z Y. Limit Theory for Mixing Dependent Sequences. Beijing: Science Press of China, 1997.

[23] 
Peligrad M, Utev S. Central limit theorem for linear processes. The Annals of Probability, 1997, 25(1): 443456.

[24] 
Thanh Lv, Yin G. Weighted sums of strongly mixing random variables with an application to nonparametric regression. Statistics and Probability Letters, 2015, 105: 195202.

[25] 
Wang X J, Hu S H. Some BaumKatz type results for φmixing random variables with different distributions. RACSAM, 2012, 106: 321331.

[26] 
Wu Q Y. Further study strong consistency of M estimator in linear model for ρmixing random samples. Journal of Systems Science and Complexity, 2011, 24: 969980.
