Results 211 to 220 of about 569,766 (265)
Characterising Epigenetic Tipping Points using a Spectral Dimension Reduction Approach. [PDF]
Bull Math BiolAlarcón T, Menendez JA, Sardanyés J.
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Risk-Resilience Feedback to Assure Critical Societal Functions. [PDF]
Risk AnalChatterjee S +3 more
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Biometrics, 1982
The simple analysis of covariance situation with two groups and one concomitant variable is considered. The parameters of this model with outliers present are estimated by the methods of at least squares and M-estimation. By use of simulation, several forms of M-estimators are compared with the least squares method.
Birch, Jeffrey B., Myers, Raymond H.
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The simple analysis of covariance situation with two groups and one concomitant variable is considered. The parameters of this model with outliers present are estimated by the methods of at least squares and M-estimation. By use of simulation, several forms of M-estimators are compared with the least squares method.
Birch, Jeffrey B., Myers, Raymond H.
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Robust Canonical Discriminant Analysis
Psychometrika, 1994A method for robust canonical discriminant analysis via two robust objective loss functions is discussed. These functions are useful to reduce the influence of outliers in the data. Majorization is used at several stages of the minimization procedure to obtain a monotonically convergent algorithm.
Verboon, Peter, van der Lans, Ivo A.
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2016
Stochastic multicriteria acceptability analysis (SMAA) is a simulation based method for discrete multicriteria decision aiding problems where information is uncertain, imprecise, or partially missing. In SMAA, different kind of uncertain information is represented by probability distributions.
Lahdelma Risto, Salminen Pekka
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Stochastic multicriteria acceptability analysis (SMAA) is a simulation based method for discrete multicriteria decision aiding problems where information is uncertain, imprecise, or partially missing. In SMAA, different kind of uncertain information is represented by probability distributions.
Lahdelma Risto, Salminen Pekka
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Philosophy of Science, 2006
Modelers often rely on robustness analysis, the search for predictions common to several independent models. Robustness analysis has been characterized and championed by Richard Levins and William Wimsatt, who see it as central to modern theoretical practice.
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Modelers often rely on robustness analysis, the search for predictions common to several independent models. Robustness analysis has been characterized and championed by Richard Levins and William Wimsatt, who see it as central to modern theoretical practice.
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Robustness analysis: a case study
Proceedings of the 27th IEEE Conference on Decision and Control, 1990Summary: Consider the stability test of polynomials whose coefficients depend multilinearly on interval parameters. This note describes and compares four brute-force solution approaches. They are applied to a simple case study example with two parameters and third-order polynomial.
Ackermann, J., Hu, H. Z., Kaesbauer, D.
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Robust Kernel Principal Component Analysis
Neural Computation, 2009This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions
Huang S.-Y., Yeh Y.-R., Eguchi S.
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