194 research outputs found
Ordinal principal component analysis for a common ranking of stochastic frontiers
No full textThe Stochastic Frontier Analysis (SFA) is a model to evaluate the Technical Efficiency (TE) for Production Units (PU). When SFA is applied on different output variables with same input, the analysis estimates different TEs for the PU. We refer to these TEs as the Multiple Technical Efficiency (MTE) of the PU. In this work, we present a method to unify the MTE in one ranking, in order to compute a synthetic index of the TE based on a parametric model. Our approach transforms the
measures of efficiency into values on an ordinal scale. Then, using the Ordinal Principal Component Analysis and a genetic algorithm, we merge the multiple rankings
PRINCIPAL COMPONENT ANALYSIS TO RANKING TECHNICAL EFFICIENCIES THROUGH STOCHASTIC FRONTIER ANALYSIS AND DEA
No full textThe Stochastic Frontier Analysis permits evaluating the Technical Efficiency scores for one output variable to obtain the corresponding Technical Efficiency of n Decision-Making Units (DMU). The objective of this work is a comparison between a Stochastic Frontier Analysis, with same input and different output variables, and the Data Envelopment
Analysis. You get k Technical Efficiency TE(yi) which are unified by a Principal Component Analysis and compared with the results of a DEA on the same data
Critical comparison of the main methods for the technical efficiency
No full textThe Technical Efficiency is a basic tool to determine the factors that slow down the production. TE aims at evaluating and comparing the operating performance of a set of production units, such as Companies, Offices, Hospi- tals, Banks, Schools, Transport Systems, etc. This paper, after an overview of the literature regarding the methodologies for measuring the Technical Ef- ficiency, compares critically the two main approaches, the Data Envelopment Analysis (DEA) and the Stochastic Frontier Analysis (SFA). These method- ologies are also discussed within an original application that targets to study the efficiency of European Countries with respect to the Gross Domestic Product (GDP)
Combined Factorial and Stochastic Frontier Analysis. Theory and an application to study the Technical Efficiency of Secondary Schools
No full textOur proposal occurs when we have more than one output variable and with Stochastic frontier Analysis (SFA) cannot be considered jointly as happens with Data Envelopment Analysis(DEA). Our goal is, therefore, to make more SFA and unify the Technical Efficiencies in a single list as for DEA. The main objective of this work is to obtain, by a genetic algorithm, a single ranking of different SFA
Technical efficiency: a critical overview of the methods
No full textTechnical Efficiency (TE) is a basic tool to determine the factors that slow down production. TE aims to evaluate and compare the operating performance of a set of production units, such as Companies, Offices, Hospitals, Banks, Schools, Transport Systems, etc. The literature on the measurement of Technical Efficiency provides a range of methodologies. This paper presents an overview of the literature on the studies, comparing the main two approaches, namely Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). DEA is the model most popular with many applications in various domains. The purpose of this paper is to provide a significant critical overview on the main pros and cons of measuring technical efficiency because less effort has been directed toward a competing efficiency model such as SFA. The target of this study is to expose critical observations to give an opportunity to be more careful in choosing the most appropriate method for evaluating Technical Efficiency
An Overview of the Data Envelopment Analysis and Stochastic Frontier Analysis
No full textThis work wants to compare the assumptions that form the basis of the two metods (SFA and DEA) in order to provide points of reflection to those who want to measure efficiency
Conjoint Representation of the Main Effects and Interaction Term in Multiple Non Symmetrical Correspondence Analysis
No full textThe main effects represent the change in the response variables due to the change on the level/categories of the predictor variables, considering the adding effects of them. By contrast the interaction effect represents the combined effect of predictor variables on the response variable. In particular, there is an interaction between two predictor variables when the effect of one predictor variable varies as the levels/categories of the other predictor vary. If the interaction is not significant, it is possible to examine the main effects. Instead, if the interaction is statistically significant and of strong entity, then, it is not useful to consider the main effects. As the matter of fact, asserting that two predictor variables interact is the same as affirming that the two variables do not have separate effects. Moreover, in this paper we suggest a procedure of the simultaneous representation of the main effect and interaction term obtained by means the decomposition of tau Gray Williams. To identify a category which is statistically significant, the confidence ellipses for a Multiple Non-Symmetric Correspondence Analysis will be shown
What influences teachers’ job satisfaction and how to improve, develop and reorganize the school activities associated with them
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The acquisition of entrepreneurial competencies in a virtual platform: a structural equation model
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