
arXiv: 1507.07902
This study proposes a robust estimator for stochastic frontier models by integrating the idea of Basu et al. [1998, Biometrika 85, 549-559] into such models. We verify that the suggested estimator is strongly consistent and asymptotic normal under regularity conditions and investigate robust properties. We use a simulation study to demonstrate that the estimator has strong robust properties with little loss in asymptotic efficiency relative to the maximum likelihood estimator. A real data analysis is performed for illustrating the use of the estimator.
43 pages, 5 figures
FOS: Computer and information sciences, stochastic frontier model, Other Statistics (stat.OT), outliers, robustness, Methodology (stat.ME), Statistics - Other Statistics, minimum density power divergence estimator, Computational methods for problems pertaining to statistics, Statistics - Methodology
FOS: Computer and information sciences, stochastic frontier model, Other Statistics (stat.OT), outliers, robustness, Methodology (stat.ME), Statistics - Other Statistics, minimum density power divergence estimator, Computational methods for problems pertaining to statistics, Statistics - Methodology
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 14 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
