By Abdelaati Daouia, Laurent Gardes (auth.), Ingrid Van Keilegom, Paul W. Wilson (eds.)
This e-book collects contributions written via recognized statisticians and econometricians to recognize Léopold Simar’s far-reaching clinical effect on data and Econometrics all through his occupation. The papers contained herein have been provided at a convention in
Louvain-la-Neuve in may possibly 2009 in honor of his retirement. The contributions hide a large number of matters surrounding frontier
estimation, which Léopold Simar has contributed a lot to during the last twenty years, in addition to similar matters comparable to semiparametric regression and types for censored data.
This ebook collects contributions written by means of famous statisticians and econometricians to recognize Léopold Simar’s far-reaching medical effect on records and Econometrics all through his occupation. The papers contained herein have been provided at a convention in
Louvain-la-Neuve in might 2009 in honor of his retirement. The contributions hide a vast number of concerns surrounding frontier
estimation, which Léopold Simar has contributed a lot to over the last 20 years, in addition to similar concerns equivalent to semiparametric regression and versions for censored data.
Read or Download Exploring Research Frontiers in Contemporary Statistics and Econometrics: A Festschrift for Leopold Simar PDF
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Additional info for Exploring Research Frontiers in Contemporary Statistics and Econometrics: A Festschrift for Leopold Simar
Sample text
We also observe significance at the 10% level for TOEs versus COEs (which are not significantly different from each other in the light industry sector). 3 also show that in comparison with the light industry ownership groups, all the ownership groups in the heavy industry sector have relatively small aggregate inefficiency scores and an (absolutely and relatively) lower level of estimated bias. These results suggest that performance varies to a lesser degree among ownership types in the heavy industry sector.
We choose the Farrell efficiency measure over others for two reasons that make it the most popular in practice. 4 Moreover, this estimator is fairly easy in terms of computation and allows for straightforward interpretation. Note that the true efficiency scores from the Farrell measure are bounded between unity and infinity, where unity represents a perfect (technical or technological) efficiency score of 100%. On the other hand, 1=TEj would represent the relative %-level of the efficiency of the j th DMU (j 2 f1; : : : ; ng/.
Daouia et al. , & Yao, F. (2008). A smooth nonparametric conditional quantile frontier estimator. Journal of Econometrics, 143, 317–333. Nadaraya, E. A. (1964). Some new estimates for distribution functions. Theory of Probability Applications, 15, 497–500. Resnick, S. I. (1987). Extreme values, regular variation, and point processes. New York: Springer. Segers, J. (2005). Generalized pickands estimators for the extreme value index. Journal of Statististical Planning and Inference, 128, 381–396.