On the efficient computation of robust regression estimators

Flores, Salvador

doi:10.1016/j.csda.2010.03.020

Cited by 11 publications

(2 citation statements)

References 20 publications

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“…Robust estimators for linear regression models including repeated median (RM) (Siegel, 1982), least median of squares (LMS) and the least trimmed squares (LTS) (Rousseeuw, 1984), S-estimate (Rousseeuw and Yohai, 1984), Fast-LTS (Rousseeuw and Driessen, 2006), efficient computation by Flores (2010), and evolutionary algorithm proposed by Nunkesser and Morell (2010) are introduced. All of them have very low efficiency for a regression model under normality assumption.…”

Section: τ −Estimate and Fast −τ −Estimatementioning

confidence: 99%

Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method

Fokalaei¹,

Shahriari

Shafaei

2014

Communications in Statistics - Theory and Methods

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Utilizing time series modeling entails estimating the model parameters and dispersion. Classical estimators for autocorrelated observations are sensitive to presence of different types of outliers and lead to bias estimation and misinterpretation. It is important to present robust methods for parameters estimation which are not influenced by contaminations. In this article, an estimation method entitled Iteratively Robust Filtered Fast−τ (IRFFT) is proposed for general autoregressive models.In comparison to other commonly accepted methods, this method is more efficient and has lower sensitivity to contaminations due to having desirable robustness properties. This has been demonstrated by applying MSE, influence function, and breakdown point criteria.

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Section: τ −Estimate and Fast −τ −Estimatementioning

confidence: 99%

Parameter Estimation of Autoregressive Models Using the Iteratively Robust Filtered Fast-τ Method

Fokalaei¹,

Shahriari

Shafaei

2014

Communications in Statistics - Theory and Methods

View full text Add to dashboard Cite

show abstract

“…Indeed, the exact computation of these estimates are often NP-hard problems and so approximate algorithms are needed in practice. Flores (2010) uses approximate reweighted least squares solutions and clustering techniques to efficiently calculate robust regression estimates while Nunkesser and Morell (2010) use evolutionary search heuristics to find robust regression solutions efficiently. A third paper by Nguyen and Welsch (2010) solves semi-definite programming problems to identify outliers and find robust regression estimates.…”

mentioning

confidence: 99%