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The data anvelopment analysis method in benchmarking of technological incubators

100%
KACZMARSKA B.
Operations Research and Decisions
|
2010
|
vol. 20
|
issue 1
79-95
EN
This paper presents an original concept for the application of Data Envelopment Analysis (DEA) in benchmarking processes within innovation and entrepreneurship centers based on the example of technological incubators. Applying the DEA method, it is possible to order analyzed objects, on the basis of explicitly defined relative efficiency, by compiling a rating list and rating classes. Establishing standards and indicating “clearances” allows the studied objects – innovation and entrepreneurship centers – to select a way of developing effectively, as well as preserving their individuality and a unique way of acting with the account of local needs.
2
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The method of data envelopment analysis as a tool for compiling a rating list

100%
GIERULSKI W.
Operations Research and Decisions
|
2010
|
vol. 20
|
issue 1
41-58
EN
The paper presents the method of Data Envelopment Analysis (DEA) as a tool for compiling rating lists in tasks of ordering elements of assemblages. We describe the differences that result from applying a one-dimensional model and multi-dimensional model, where in the latter a so-called partial order is created for incomparable elements in the assemblage being ordered. The example used to illustrate the issues discussed is the analysis of teaching results in a student group.
3
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FAMILIES OF CLASSIFIERS – APPLICATION IN DATA

75%
Grzybowska U., Karwański M.
Metody Ilościowe w Badaniach Ekonomicznych
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2014
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vol. 15
|
issue 2
94-101
EN
Economic description of firms and companies is based on a number of indicators. The indicators are related to each other and can be considered only in a specific context. Regression models allow for such approach. Unfortunately, the problems we deal with are usually nonlinear and the choice of relevant information is very difficult. The aim of the paper is to present a method of variable selection based on random forest and gradient boosting approach and its application to companies ranking in DEA method. The results will be compared with the ordering obtained using expert supported approach for variable selection in DEA.
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