Estimating a Changepoint, Boundary of Frontier in the Presence of Observation Error
AbstractWhen stochastic errors are added to data from a distribution with a sharp boundary, such as a changepoint or a frontier, nonparametric estimation of the boundary can be interpreted as a problem of deconvolution. We argue that, rather than attempting to estimate the distribution of the uncorrupted data, and thereby approximate the boundary, one might focus more directly on the boundary estimation problem.
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Bibliographic InfoPaper provided by Catholique de Louvain - Institut de statistique in its series Papers with number 0012.
Length: 28 pages
Date of creation: 2000
Date of revision:
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Postal: Universite Catholique de Louvain, Institut de Statistique, Voie du Roman Pays, 34 B-1348 Louvain- La-Neuve, Belgique.
DISTRIBUTION ; EVALUATION ; PRODUCTION ; BOUNDARIES;
Other versions of this item:
- Hall P. & Simar L., 2002. "Estimating a Changepoint, Boundary, or Frontier in the Presence of Observation Error," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 523-534, June.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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- Udhayakumar, A. & Charles, V. & Kumar, Mukesh, 2011. "Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems," Omega, Elsevier, vol. 39(4), pages 387-397, August.
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- Florens, Jean-Pierre & Simar, Leopold, 2005. "Parametric approximations of nonparametric frontiers," Journal of Econometrics, Elsevier, vol. 124(1), pages 91-116, January.
- Yuen, Andrew Chi-lok & Zhang, Anming & Cheung, Waiman, 2013. "Foreign participation and competition: A way to improve the container port efficiency in China?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 220-231.
- Goldenshluger, A. & Tsybakov, A., 2004. "Estimating the endpoint of a distribution in the presence of additive observation errors," Statistics & Probability Letters, Elsevier, vol. 68(1), pages 39-49, June.
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