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dc.contributor.authorParavee Maneejuken_US
dc.contributor.authorWoraphon Yamakaen_US
dc.description.abstract© 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. This study aims to improve the copula-based stochastic frontier quantile model by treating the quantile as the unknown parameter. This method can solve the problem of quantile selection bias as the quantile will be estimated simultaneously with other parameters in the model. We then evaluate the performance and accuracy of the proposed model by conducting two simulation studies and a real data analysis with two different data sets. The overall results reveal that our proposed model can beat the conventional stochastic frontier model and also the copula-based stochastic frontier model with a given quantile.en_US
dc.subjectComputer Scienceen_US
dc.titleCopula-Based Stochastic Frontier Quantile Model with Unknown Quantileen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume898en_US Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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