Article

Full entry | PDF   (2.2 MB)
References:
[1] D. M. Bates, D. G. Watts: Nonlinear Regression Analysis and Its Applications. J. Wiley \& Sons, New York 1988. MR 1060528 | Zbl 0728.62062
[2] P. J. Bickel, M. J. Wichura: Convergence criteria for multiparameter stochastic processes and some applications. Ann. Math. Statist. 42 (1971), 1656-1670. MR 0383482 | Zbl 0265.60011
[3] M. Csörgö, P. Révész: Strong Approximation in Probability and Statistics. Akademia Kiadó, Budapest 1981. MR 0666546
[4] P. L. Davis: Aspects of robust linear regression. Ann. Statist. 21 (1993), 1843-1899. MR 1245772
[5] F. R. Hampel E. M. Ronchetti P. J. Rousseeuw, W. A. Stahel: Robust Statistics -- The Approach Based on Influence Functions. J. Wiley \& Sons, New York 1986. MR 0829458
[6] P. J. Huber: Robust estimation of a location parameter. Ann. Math. Statist. 35 (1964), 73-101. MR 0161415 | Zbl 0136.39805
[7] J. Jurečková: Consistency of $M$-estimators in linear model generated by non-monotone and discontinuous $\psi$-functions. Probab. Math. Statist. 10 (1988), 1-10. MR 0990395
[8] J. Jurečková, B. Procházka: Regression quantiles and trimmed least squares estimator in nonlinear regression model. Nonparametric Statist. 3 (1994), 201-222. MR 1291545
[9] J. Jurečková, P. K. Sen: Uniform second order asymptotic linearity of $M$-statistics in linear models. Statist. Decisions 7 (1989), 263-276. MR 1029480
[10] J. Jurečková, A. H. Welsh: Asymptotic relations between $L$- and $M$-estimators in the linear model. Ann. Inst. Statist. Math. 42 (1990), 671-698. MR 1089470
[11] F. Liese, I. Vajda: Consistency of $M$-estimators in general models. J. Multivariate Anal. 50 (1994), 93-114. MR 1292610
[12] A. Marazzi: Algorithms, Routines and S Functions for Robust Statistics. Wadsworth \& Brooks/Cole Advanced Books \& Software, Pacific Grove, California 1992. MR 1184396
[13] J. M. Ortega, W. C. Rheinboldt: Iterative Solution of Nonlinear Equations in Several Variables. Academic Press, New York and London 1970. MR 0273810 | Zbl 0241.65046
[14] S. Portnoy: Tightness of the sequence of empiric c.d.f. processes defined from regression fractiles. In: Robust and Nonlinear Time-Series Analysis (J. Franke, W. Hardle, D. Martin, eds.), Springer-Verlag, New York, 1983, pp. 231-246. MR 0786311
[15] C. R. Rao, L. C. Zhao: On the consistency of $M$-estimate in linear model obtained through an estimating equation. Statist. Probab. Lett. 14 (1992), 79-84. MR 1172294
[16] A. Rubio L. Aguilar, J. Á. Víšek: Testing for difference between models. Comput. Statist. 8 (1992), 57-70. MR 1220337
[17] A. Rubio F. Quintana, J. Á. Víšek: Test for differences of $M$-estimates between nonlinear regression models. Probab. Math. Statist. 14 (1993), 2, 189-206. MR 1321760
[18] J. Á. Víšek: Stability of regression models estimates with respect to subsamples. Computat. Statist. 7 (1992), 183-203. MR 1178353
[19] J. Á. Víšek: Problems connected with selection of robust procedure. In: Proceedings of PROBASTAT'91 (A. Pázman and J. Volaufová, eds.), Printing House of the Technical University of Liptovský Mikuláš 1992, pp. 189-203.
[20] J. Á. Víšek: On the role of contamination level and the least favorable behaviour of gross-error sensitivity. Probab. Math. Statist. 14 (1993), 2, 173-187. MR 1321759
[21] J. Á. Víšek: A cautionary note on the method of Least Median of Squares reconsidered. In: Transactions of the Twelfth Prague Conference on Information Theory, Statistical Decision Functions and Random Processes (J. Á. Víšek and P. Lachout, eds.), Prague 1994, pp. 254-259.
[22] J. Á. Víšek: Sensitivity analysis of $M$-estimates. Ann. Inst. Statist. Math., to appear. MR 1424776

Partner of