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-Consistent robust integration-based estimation

Author

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  • Jun, Sung Jae
  • Pinkse, Joris
  • Wan, Yuanyuan

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 -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.

Suggested Citation

  • Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2011. "-Consistent robust integration-based estimation," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 828-846, April.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:4:p:828-846
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    References listed on IDEAS

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    1. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    2. Zinde-Walsh, Victoria, 2002. "Asymptotic Theory For Some High Breakdown Point Estimators," Econometric Theory, Cambridge University Press, vol. 18(5), pages 1172-1196, October.
    3. Shinichi Sakata & Halbert White, 1998. "High Breakdown Point Conditional Dispersion Estimation with Application to S&P 500 Daily Returns Volatility," Econometrica, Econometric Society, vol. 66(3), pages 529-568, May.
    4. Krasker, William S, 1980. "Estimation in Linear Regression Models with Disparate Data Points," Econometrica, Econometric Society, vol. 48(6), pages 1333-1346, September.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. Hossjer, O. & Croux, C. & Rousseeuw, P. J., 1994. "Asymptotics of Generalized S-Estimators," Journal of Multivariate Analysis, Elsevier, vol. 51(1), pages 148-177, October.
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