855 research outputs found
Robust estimation in simultaneous equations models
In this paper we review existing work on robust estimation for simultaneous equations models. Then we discuss three strategies for obtaining estimators with a high breakdown point, a controllable efficiency, and a reasonable computational cost: (a) robustifying Three-Stages Least Squares, (b) robustifying the Full Information Maximum Likelihood method by minimizing the determinant of a robust covariance matrix of residuals, and (c) generalizing multivariate tauestimators (Lopuhaa 1991) to these models. The latter seems the most promising approach
Classification of Systematic Measurement Errors within the Framework of Robust Data Reconciliation
A robust data reconciliation strategy provides unbiased variable estimates in the presence of a moderate quantity of atypical measurements. However, estimates get worse if systematic measurement errors that persist in time (e.g., biases and drifts) are undetected and the breakdown point of the robust strategy is surpassed. The detection and classification of those errors allow taking corrective actions on the inputs of the robust data reconciliation that preserve the instrumentation system redundancy while the faulty sensor is repaired. In this work, a new methodology for variable estimation and systematic error classification, which is based on the concepts of robust statistics, is presented. It has been devised to be part of the real-time optimization loop of an industrial plant; therefore, it runs for processes operating under steady-state conditions. The robust measurement test is proposed in this article and used to detect the presence of sporadic and continuous systematic errors. Also, the robust linear regression of the data contained in a moving window is applied to classify the continuous errors as biases or drifts. Results highlight the performance of the proposed methodology to detect and classify outliers, biases, and drifts for linear and nonlinear benchmarks.Fil: Llanos, Claudia Elizabeth. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca. Planta Piloto de IngenierĂa QuĂmica. Universidad Nacional del Sur. Planta Piloto de IngenierĂa QuĂmica; ArgentinaFil: Sanchez, Mabel Cristina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca. Planta Piloto de IngenierĂa QuĂmica. Universidad Nacional del Sur. Planta Piloto de IngenierĂa QuĂmica; ArgentinaFil: Maronna, Ricardo Antonio. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Matemáticas; Argentin
Comments on: Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination
El autor realiza dos observaciones acerca del artĂculo "Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination" (10.1007/s11749-015-0450-6).Facultad de Ciencias Exacta
Recent Results on Robust Estimation in Multivariate Analysis
Classic methods in multivariate analysis require the estimat.ion of mean vectors and covariance matrices, and their results can therefore be substantially altered by a small proportion of atypical observations ( "outliers")
Continuity and differentiability of regression M functionals
This paper deals with the Fisher-consistency, weak continuity and
differentiability of estimating functionals corresponding to a class of both
linear and nonlinear regression high breakdown M estimates, which includes S
and MM estimates. A restricted type of differentiability, called weak
differentiability, is defined, which suffices to prove the asymptotic normality
of estimates based on the functionals. This approach allows to prove the
consistency, asymptotic normality and qualitative robustness of M estimates
under more general conditions than those required in standard approaches. In
particular, we prove that regression MM-estimates are asymptotically normal
when the observations are -mixing.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ368 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
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