Guest Editorial
Tags: REGRESSION analysis; ROBUST statistics
Related Articles
- Bouligand Derivatives and Robustness of Support Vector Machines for Regression. Christmann, Andreas; Van Messem, Arnout; Bartlett, Peter // Journal of Machine Learning Research;5/1/2008, Vol. 9 Issue 5, p915
We investigate robustness properties for a broad class of support vector machines with non-smooth loss functions. These kernel methods are inspired by convex risk minimization in infinite dimensional Hilbert spaces. Leading examples are the support vector machine based on the e-insensitive loss...
- A Simulation Study on Robust Alternatives of Least Squares Regression. Mohebbi, M.; Nourijelyani, K.; Zeraati, H. // Journal of Applied Sciences;2007, Vol. 7 Issue 22, p3469
We applied four methods of linear regression; the least squares, Huber M, least absolute deviations and nonparametric to several distributional assumptions. The same sets of simulated data were used and MSE, MAD and biases of these methods were compared. The least absolute deviations, Huber M...
- Lock, stock, and barrel: a comprehensive assessment of the determinants of terror. Gassebner, Martin; Luechinger, Simon // Public Choice;Dec2011, Vol. 149 Issue 3/4, p235
We assess the robustness of previous findings on the determinants of terrorism. Using extreme bound analysis, the three most comprehensive terrorism datasets, and focusing on the three most commonly analyzed aspects of terrorist activity, i.e., location, victim, and perpetrator, we re-assess the...
- Model Selection in Kernel Based Regression using the Influence Function. Debruyne, Michiel; Hubert, Mia; Suykens, Johan A. K. // Journal of Machine Learning Research;10/1/2008, Vol. 9 Issue 10, p2377
Recent results about the robustness of kernel methods involve the analysis of influence functions. By definition the influence function is closely related to leave-one-out criteria. In statistical learning, the latter is often used to assess the generalization of a method. In statistics, the...
- Robust statistics for ionosphere data: from Rawer's nombre de corrélation (1951) to maximum-depth regression technique (1998). Taubenheim, J. // Advances in Radio Science;2004, Vol. 2, p259
Contrast between "normal" and "disturbed" states of the ionosphere early induced the suggestion to present ionospheric data with the aid of a "robust" (i.e. outlier-resistant) statistic, namely, the median instead of the conventional arithmetic mean. K. Rawer, in 1951, defined on this concept a...
- Robust second-order least-squares estimator for regression models. Chen, Xin; Tsao, Min; Zhou, Julie // Statistical Papers;May2012, Vol. 53 Issue 2, p371
The second-order least-squares estimator (SLSE) was proposed by Wang (Statistica Sinica 13:1201-1210, 2003) for measurement error models. It was extended and applied to linear and nonlinear regression models by Abarin and Wang (Far East J Theor Stat 20:179-196, 2006) and Wang and Leblanc (Ann...
- Robust Logistic Diagnostic for the Identification of High Leverage Points in Logistic Regression Model. Syaiba, B. A.; Habshah, M. // Journal of Applied Sciences;2010, Vol. 10 Issue 23, p3042
No abstract available.
- A Simple Approach to Robust Inference in a Cointegrating System. Wright, Jonathan H. // Working Papers -- U.S. Federal Reserve Board's International Fin;1999, p1
This paper examines a simple approach to robust inference in a cointegrating system. Cointegration requires all the variables in the system to have exact unit roots. It is traditional for researchers to test for a unit root in each variable prior to a cointegration analysis. Meanwhile,...
- Robust construction of regression models based on the generalized least absolute deviations method. Tyrsin, A. N. // Journal of Mathematical Sciences;Dec2006, Vol. 139 Issue 3, p6634
We suggest a new method of robust construction of linear regression models, namely, the generalized least absolute deviations method. We give a theoretical justification of the method and consider its experimental approbation. Bibliography: 12 titles.
