N-Consistent robust integration-based estimation

Sung Jae Jun, Joris Pinkse, Yuanyuan Wan

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We propose a new robust estimator of the regression coefficients in a linear regression model. The proposed estimator is the only robust estimator based on integration rather than optimization. It allows for dependence between errors and regressors, is n-consistent, and asymptotically normal. Moreover, it has the best achievable breakdown point of regression invariant estimators, has bounded gross error sensitivity, is both affine invariant and regression invariant, and the number of operations required for its computation is linear in n. An extension would result in bounded local shift sensitivity, also.

Original languageEnglish (US)
Pages (from-to)828-846
Number of pages19
JournalJournal of Multivariate Analysis
Volume102
Issue number4
DOIs
StatePublished - Apr 1 2011

Fingerprint

Robust Estimators
Gross Error Sensitivity
Regression
Estimator
Breakdown Point
Affine Invariant
Invariant
Regression Coefficient
Linear Regression Model
Linear regression
Optimization
Robust estimators

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Cite this

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N-Consistent robust integration-based estimation. / Jun, Sung Jae; Pinkse, Joris; Wan, Yuanyuan.

In: Journal of Multivariate Analysis, Vol. 102, No. 4, 01.04.2011, p. 828-846.

Research output: Contribution to journalArticle

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