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Mathematics
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Distribution Theory and Stochastic Frontier Analysis
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Distribution Theory and Stochastic Frontier Analysis

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 5232

Special Issue Editors

Prof. Dr. Filippo Domma

E-Mail Website
Guest Editor
Department of Economics, Statistics and Finance, University of Calabria, Via Pietro Bucci, Cubo 0C, 87036, Arcavacata di Rende (CS), Italy
Interests: distribution theory; copula function; reliability theory and survival analysis; stress-strength model; frailty models; stochastic frontiers analysis; income and poverty
Dr. Graziella Bonanno

E-Mail Website
Guest Editor
Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II 132 – 84084, Fisciano (SA), Italy
Interests: economic efficiency and quantitative analysis of productive systems; banking and airports efficiency; international trade; regional divide; firms behavior (with focus on innovation); internationalization strategies; micro-econometric analysis

Special Issue Information

The purpose of this Special Issue is to collect a set of papers that focus their attention on the latest developments in modeling the error terms of the stochastic frontiers (SF) with more flexible distribution functions capable of capturing different characteristics highlighted by the observed data.

In recent literature, some more general specifications of the distribution of errors, used in the construction of the SF, have allowed some specific characteristics that were previously neglected to be modeled and some problems, such as the wrong skewness problem and dependence between the two error terms, to be investigated.

Stochastic frontier models find application in all fields of Economics and other social sciences, Statistics, Engineering, Medicine and Biomedicine, Business, and also in the art sector. Special attention will be reserved for papers with new fields of application of the methodology and philosophy underlying the SF and papers that use SF in the case of high-dimensional data.

Some subjects included in the “Distribution Theory and Stochastic Frontier Analysis” Special Issue will be wrong skewness problem, spatial dependence, dependence between the inefficiency term and random error, endogeneity, and time dependence.

Prof. Dr. Filippo Domma
Dr. Graziella Bonanno
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • stochastic frontier approach
  • efficiency
  • random error
  • composed error
  • distributions
  • wrong skewness
  • dependence
  • copula functions
  • asymmetry
  • left-skewed
  • right-skewed
  • exponential
  • normal
  • half-normal

Published Papers (2 papers)

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Research

15 pages, 415 KiB  
Article
Measuring Efficiency in the Summer Olympic Games Disciplines: The Case of the Spanish Athletes
by Emilio Gómez-Déniz, Nancy Dávila-Cárdenes, Alejandro Leiva-Arcas and María J. Martínez-Patiño
Mathematics 2021, 9(21), 2688; https://doi.org/10.3390/math9212688 - 22 Oct 2021
Cited by 2 | Viewed by 2219
Abstract
This paper estimates the technical efficiency of Olympic disciplines in which Spanish athletes participate, taking into account the results obtained in the last three Olympic Games. A stochastic production frontier model (normal-exponential), using two control variables linked to economic factors such as budget [...] Read more.
This paper estimates the technical efficiency of Olympic disciplines in which Spanish athletes participate, taking into account the results obtained in the last three Olympic Games. A stochastic production frontier model (normal-exponential), using two control variables linked to economic factors such as budget and sports scholarships, is estimated in order to obtain different Olympic sports’ efficiencies distinguished by gender, using data from 2005 to 2016. The results detect some differences among the considered disciplines. In all the cases, athletics, canoeing, cycling, swimming, and tennis, depending on the gender, reach better values. This paper’s novelty lies in the efficiency analysis carried out on the Olympic disciplines and athletes of a country and not on the country’s efficiency, which allows managers and stakeholders to decide about investments concerning disciplines and athletes. Full article
(This article belongs to the Special Issue Distribution Theory and Stochastic Frontier Analysis)
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21 pages, 845 KiB  
Article
The Assessment of Car Making Plants with an Integrated Stochastic Frontier Analysis Model
by Seog-Chan Oh and Jaemin Shin
Mathematics 2021, 9(11), 1296; https://doi.org/10.3390/math9111296 - 5 Jun 2021
Cited by 4 | Viewed by 2667
Abstract
As global competition has intensified in the automotive industry, there is a strong need for management teams to develop methods that allow accurate and objective assessments of plant productivity and to identify productivity improvement opportunities for the best manufacturing practices. Stochastic frontier analysis [...] Read more.
As global competition has intensified in the automotive industry, there is a strong need for management teams to develop methods that allow accurate and objective assessments of plant productivity and to identify productivity improvement opportunities for the best manufacturing practices. Stochastic frontier analysis (SFA) models have been used as a statistical benchmarking tool to provide a bird’s-eye view of an industrial sector. SFA models can also be adapted for plant productivity assessment. However, owing to the problem of multicollinearity, the general form of SFA is difficult to apply to the assessment of complex manufacturing systems in the automotive industry, which is characterized by many control and external factors that are intercorrelated to each other. This study proposes a method for applying SFA to vehicle manufacturing plants with a focus on gaining high accuracy in model parameter estimation, by decomposing a plant into components (i.e., shops), building an SFA model for each shop, and reintegrating the general plant system through the appropriate combination of shop-level inefficiency distributions. In particular, this study focuses on documenting the derivation of a new probability density function that integrates three different inefficiency distributions. For illustration of the proposed approach, hypothetical vehicle assembly plants are assessed as examples, where the total labor hours are split into Bodyshop, Paintshop, and General Assembly, exclusively and collectively. Finally, this study offers a solution process to clarify the reasons for underperforming plants in terms of labor productivity and identify the course of actions to cure the issues with some managerial insights emphasizing the balanced approach, incorporating people, process and technology. Full article
(This article belongs to the Special Issue Distribution Theory and Stochastic Frontier Analysis)
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