Statistical learning for recommending (robust) nonlinear regression methods

Baldauf, M. and Silva, J. 2012. On the use of robust regression in econometrics. Economic Letters 114, 124–127.Search in Google Scholar

Brazdil, P., Giraud-Carrier, C., Soares, C., and Vilalta, E. 2009. Metalearning: Applications to data mining. Springer, Berlin.10.1007/978-3-540-73263-1Search in Google Scholar

Cížek, P. 2011. Semiparametrically weighted robust estimation of regression models. Computational Statistics & Data Analysis 55, 774–788.Search in Google Scholar

Fasano, V., Maronna, R., Sued, M., and Yohai, V. 2012. Continuity and differentiability of regression M-functionals. Bernoulli 18, 1284–1309.Search in Google Scholar

Guo, Y., Hastie, T., and Tibshirani, R. 2007. Regularized discriminant analysis and its application in microarrays. Biostatistics 8, 86–100.Search in Google Scholar

Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., and Stahel, W. A. 1986. Robust statistics—The approach based on influence functions. Wiley, New York.Search in Google Scholar

Hastie, T., Tibshirani, R., and Friedman, J. 2008. The elements of statistical learning, 2nd ed. Springer, New York.10.1007/978-0-387-84858-7Search in Google Scholar

Kalina, J. 2012. Highly robust statistical methods in medical image analysis. Biocybernetics and Biomedical Engineering 32, 3–16.Search in Google Scholar

Kalina, J. 2014. On robust information extraction from high-dimensional data. Serbian Journal of Management 9, 131–144.Search in Google Scholar

Kalina, J. and Peštová, B. 2017. Robust regression estimators: A comparison of prediction performance. In 35th International Conference Mathematical Methods in Economics MME 2017. University of Hradec Králové, Hradec Králové, 307–312.Search in Google Scholar

Kalina, J., Vašaničová, P., and Litavcová, E. 2019. Regression quantiles under heteroscedasticity and multicollinearity: Analysis of travel and tourism competitiveness. Ekonomický časopis/Journal of Economics 67, 69–85.Search in Google Scholar

Koenker, R. and Park, B. 1996. An interior point algorithm for nonlinear quantile regression. Journal of Econometrics 71, 265–283.Search in Google Scholar

Maronna, R., Martin, D., and Yohai, V. 2006. Robust statistics: Theory and methods. Wiley, New York.10.1002/0470010940Search in Google Scholar

Mount, D., Netanyahu, N., Piatko, C., Silverman, R., and Wu, A. 2014. On the least trimmed squares estimator. Algorithmica 69, 148–183.Search in Google Scholar

Riazoshams, H., Midi, H., and Sharipov, O. 2010. The performance of robust two-stage estimator in nonlinear regression with autocorrelated error. Communications in Statistics – Simulation and Computation 39, 1251–1268.Search in Google Scholar

Riechmann, T. 2001. Learning in economics. Analysis and application of genetic algorithms. Springer, Berlin.Search in Google Scholar

Rousseeuw, P. J. 1983. Multivariate estimation with high breakdown point. In Proceedings of the 4th Pannonian Symposium on Mathematical Statistics, W. Grossmann, G. Pflug, I. Vincze, and W. Wertz, Eds. D. Reidel, Dordrecht, 283–297.Search in Google Scholar

Rousseeuw, P. J. and van Driessen, K. 2006. Computing lts regression for large datasets. Data Mining and Knowledge Discovery 12, 29–45.Search in Google Scholar

Seber, G. and Wild, C. 2003. Nonlinear regression. Wiley, New York.Search in Google Scholar

Smith-Miles, K., Baatar, D., Wreford, B., and Lewis, R. 2014. Towards objective measures of algorithm performance across instance space. Computers & Operations Research 45, 12–24.Search in Google Scholar

Stromberg, A. and Ruppert, D. 1992. Breakdown in nonlinear regression. Journal of the American Statistical Association 87, 991–997.Search in Google Scholar

Vasant, P. M. 2012. Meta-heuristics optimization algorithms in engineering, business, economics, and finance. IGI Global, Hershey.10.4018/978-1-4666-2086-5Search in Google Scholar

Víšek, J. Á. 2011. Consistency of the least weighted squares under heteroscedasticity. Kybernetika 47, 179–206.Search in Google Scholar

Wooldridge, J. M. 2001. Econometric analysis of cross section and panel data. MIT Press, Cambridge.Search in Google Scholar