[1] R. J. Adcock, Note on the method of least squares, Analyst 4(6)
(1977), 183-184.
[2] C. L. Cheng and J. W. Van Ness, Statistical Regression with
Measurement Error, Arnold and New York: Oxford University Press,
London, 1999.
[3] C. L. Cheng and J. Riu, On estimating linear relationships when
both variables are subject to heteroscedastic measurement errors,
Technometrics 48(4) (2006), 511-519.
[4] W. A. Fuller, Measurement Error Models, John Wiley & Sons, New
York, 2009.
[5] J. W. Gillard and T. C. Iles, Method of moments estimation in
linear regression with errors in both variables, Cardiff University,
School of Mathematics, Technical Paper, 2005.
[6] G. H. Golub and C. F. Van Loan, An analysis of the total least
squares problem, SIAM Journal on Numerical Analysis 17(6) (1980),
883-893.
[7] P. J. Huber, Robust regression: Asymptotics, conjectures and Monte
Carlo, The Annals of Statistics (1973), 799-821.
[8] P. J. Huber, Robust Statistics, Springer, Berlin Heidelberg,
2011.
[9] C. H. Kummel, Reduction of observed equations which contain more
than one observed quantity, The Analyst (6) (1979), 97-105.
[10] V. Mahboub, A. R. Amiri-Simkooei and M. A. Sharifi, Iteratively
reweighted total least squares: A robust estimation in errors
in-variables models, Survey Review 45(329) (2013), 92-99.
[11] B. Schaffrin, I. Lee, Y. Felus and Y. Choi, Total least-squares
for geodetic straight-line and plane adjustment, Bollettino Di
Geodesia E Scienze Affini 65(3) (2006), 141-168.
[12] B. Schaffrin and A. Wieser, On weighted total least-squares
adjustment for linear regression, Journal of Geodesy 82(7) (2008),
415-421.
[13] M. Y. Wong, Likelihood estimation of a simple linear regression
model when both variables have error, Biometrika 76(1) (1989),
141-148.