2017
DOI: 10.1002/cjs.11315
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Depth‐weighted robust multivariate regression with application to sparse data

Abstract: A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re‐w… Show more

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Cited by 5 publications
(1 citation statement)
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“…Bias correction for the maximum likelihood estimator and adjustments of the likelihood-ratio statistics are also derived for this general model (see [38], [37]). The elliptical distributions can also be used as the basis to consider robustness in multivariate linear regression, as in [14], [21], [29], [36], [42], [50] and many others. Nevertheless the envelope model under the context of elliptical multivariate regression has not yet been implemented.…”
Section: Introductionmentioning
confidence: 99%
“…Bias correction for the maximum likelihood estimator and adjustments of the likelihood-ratio statistics are also derived for this general model (see [38], [37]). The elliptical distributions can also be used as the basis to consider robustness in multivariate linear regression, as in [14], [21], [29], [36], [42], [50] and many others. Nevertheless the envelope model under the context of elliptical multivariate regression has not yet been implemented.…”
Section: Introductionmentioning
confidence: 99%