D. M. Bates and D. G. Watts, Relative curvature measures of nonlinearity, Journal of Royal Statistical Society, vol.42, pp.1-25, 1980.

G. Chavent, Local stability of the output least square parameter estimation technique, Matematicada Applicada e Computacional, pp.3-22, 1983.
URL : https://hal.archives-ouvertes.fr/inria-00076424

G. Chavent, A new sufficient condition for the wellposedness of non-linear least-square problems arising in identification and control, Analysis and Optimization of Systems, pp.452-463, 1990.
URL : https://hal.archives-ouvertes.fr/inria-00075438

G. Chavent, New Size $ \times $ Curvature Conditions for Strict Quasiconvexity of Sets, SIAM Journal on Control and Optimization, vol.29, issue.6, pp.1348-1372, 1991.
DOI : 10.1137/0329069

M. Clyde and K. Chaloner, Constrained design strategies for improving normal approximations in nonlinear regression problems, Journal of Statistical Planning and Inference, vol.104, issue.1, pp.175-196, 2002.
DOI : 10.1016/S0378-3758(01)00239-7

E. Z. Demidenko, Optimization and Regression, 1989.

E. Z. Demidenko, Is this the least squares estimate?, Biometrika, vol.87, issue.2, pp.437-452, 2000.
DOI : 10.1093/biomet/87.2.437

V. F. Dem-'yanov and V. N. Malozemov, Introduction to Minimax, 1974.

X. Li and S. Fang, On the entropic regularization method for solving min-max problems with applications, Mathematical Methods of Operations Research, vol.19, issue.1, pp.119-130, 1997.
DOI : 10.1007/BF01199466

A. Pázman, Nonlinear least squares - uniqueness versus ambiguity, Series Statistics, vol.40, issue.3, pp.323-336, 1984.
DOI : 10.1214/aoms/1177697731

A. Pázman, Nonlinear Statistical Models, 1993.
DOI : 10.1007/978-94-017-2450-0