scholarly journals Simplifying Bias Correction for Selective Sampling: A Unified Distribution-Free Approach to Handling Endogenously Selected Samples

2021 ◽  
Author(s):  
Yi Qian ◽  
Hui Xie
Keyword(s):  
Bias Correction ◽  
Distribution Free ◽  
Selective Sampling ◽  
Distribution Free Approach

Cost and Profit Efficiencies in the Spanish Hotel Industry

2015 ◽  
Vol 41 (8) ◽  
pp. 985-1006 ◽  
Author(s):  
Antonio Arbelo ◽  
Pilar Pérez-Gómez ◽  
Enrique González-Dávila ◽  
Felipe Manuel Rosa-González
Keyword(s):  
Competitive Advantage ◽  
Special Interest ◽  
Cost Efficiency ◽  
Hotel Industry ◽  
Profit Efficiency ◽  
Distribution Free ◽  
Working Hypothesis ◽  
Distribution Free Approach ◽  
Hotel Sector

This article focuses on estimating and discussing cost and profit efficiencies related with the Spanish hotel sector. Managers have a special interest in controlling costs as a source of competitive advantage, which may enable companies to improve their results in a continuous manner. However, they usually do not attribute much importance to the improvement of profit efficiency, which is generally much lower than cost efficiency and as a result vital for achieving competitive advantage. In this article, cost and profit efficiencies are estimated in the Spanish hotel sector using a data panel for the years 2007 to 2011 and using distribution free approach methodology. The results show profit efficiency levels significantly lower than levels of cost efficiency, thereby confirming our working hypothesis.

scholarly journals A Distribution-Free Approach to Stochastic Efficiency Measurement with Inclusion of Expert Knowledge

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Kerry Khoo-Fazari ◽  
Zijiang Yang ◽  
Joseph C. Paradi
Keyword(s):  
Data Envelopment Analysis ◽  
Quantitative Data ◽  
Expert Knowledge ◽  
Simulated Dataset ◽  
Data Envelopment ◽  
Nonparametric Modeling ◽  
Distribution Free ◽  
New Frontier ◽  
Distribution Free Approach ◽  
Computationally Intensive

This paper proposes a new efficiency benchmarking methodology that is capable of incorporating probability while still preserving the advantages of a distribution-free and nonparametric modeling technique. This new technique developed in this paper will be known as the DEA-Chebyshev model. The foundation of DEA-Chebyshev model is based on the model pioneered by Charnes, Cooper, and Rhodes in 1978 known as Data Envelopment Analysis (DEA). The combination of normal DEA with DEA-Chebyshev frontier (DCF) can successfully provide a good framework for evaluation based on quantitative data and qualitative intellectual management knowledge. The simulated dataset was tested on DEA-Chebyshev model. It has been statistically shown that this model is effective in predicting a new frontier, whereby DEA efficient units can be further differentiated and ranked. It is an improvement over other methods, as it is easily applied, practical, not computationally intensive, and easy to implement.

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