Stochastic frontier models have been considered as an alternative to deterministic frontier models in that they attribute the deviation of the output from the production frontier to both measurement error and inefficiency. However, such merit is often dimmed by strong assumptions on the distribution of the measurement error and the inefficiency such as the normal-half normal pair or the normal-exponential pair. Since the distribution of the measurement error is often accepted as being approximately normal, here we show how to estimate various stochastic frontier models with a relaxed assumption on the inefficiency distribution, building on the recent work of Kneip and his coworkers. We illustrate the usefulness of our method with data on Japanese local public hospitals.

中文翻译：

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放宽随机前沿模型的分布假设

随机边界模型被认为是确定性边界模型的替代方法，因为它们将生产边界的输出偏差归因于测量误差和效率低下。但是，对于测量误差的分布和效率低下的假设（例如正常对半正常对或正常对指数对），这些优点通常会被削弱。由于测量误差的分布通常被认为是近似正态分布的，因此在此我们展示了如何基于Kneip及其同事的最新工作，在对效率分布的宽松假设下估算各种随机边界模型。我们用日本当地公立医院的数据说明了我们方法的有效性。