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Robust Regression

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
Discriminative methods (e.g., kernel regression, SVM) have been extensively used to solve problems such as object recognition, image alignment and pose estimation from images. These methods typically map image features ( X) to continuous (e.g., pose) or discrete (e.g., object category) values. A major drawback of existing discriminative methods is that
Dong, Huang   +2 more
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Robust dynamics

Nature Chemistry, 2010
Although metal-organic frameworks are extensive in number and have found widespread applications, there remains a need to add complexity to their structures in a controlled manner. It is inevitable that frameworks capable of dynamics will be required. However, as in other extended structures, when they are flexible, they fail.
Hexiang, Deng   +3 more
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Developmental Robustness

Annals of the New York Academy of Sciences, 2002
Abstract: Developmental robustness, the capacity to stay “on track” despite the myriad vicissitudes that inevitably plague a developing organism, is, I argue, a prerequisite for natural selection and key to our understanding of the evolution of developmental processes. But how is such robustness achieved?
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Estimating robustness

Journal of Economic Theory, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robust heteroskedasticity-robust tests

Economics Letters, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robust Preferences and Robust Portfolio Choice

2009
This chapter focuses on the problems of robust preferences and robust portfolio choice. The problem of portfolio choice consists in choosing, among all the available positions, a position that is affordable, given the investor's wealth w , and which is optimal with respect to the investor's preferences.
Föllmer, Hans   +2 more
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Robust regression through robust covariances

Communications in Statistics - Theory and Methods, 1986
This paper discusses the estimation of regression parameters after summarizing the data by a covariance matrix of the concatenated vector of explanatory variables and response variable. A robust estimate of the covariance matrix leads to a robust regression estimator.
Ricardo Maronna, Stephan Morgenthaler
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Model robustness in robust identification

Proceedings of 1995 34th IEEE Conference on Decision and Control, 2002
Considers the issue of model robustness. In particular the author considers fitting a linear transfer function model in the presence of a small deviation from linearity. The issue of deviation from linearity is often mentioned in robust identification but does not seem to have been explicitly treated before.
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Robustness and the robust estimate

Journal of Geodesy, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robust Stability and Robust Stabilizability

2002
Most real systems cannot be represented by linear dynamics, but sometimes, under some assumptions, it is possible to model the dynamical behavior of practical systems with a linear model having some uncertainties. The presence of these uncertainties in the dynamics requires the establishment of robust conditions that can guarantee the stability and/or ...
El-Kébir Boukas, Zi-Kuan Liu
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