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|Authors:||Enrico Ciavolino: Researcher of Statistics, University of Salento, Department of Philosophy & Social Science, Italy|
|Publication title:||Modelling GME and PLS Estimation Methods for Evaluating the Job Satisfaction in the Public Sector|
|Conference:||10th QMOD Conference. Quality Management and Organiqatinal Development. Our Dreams of Excellence, 18-20 June, 2007 in Helsingborg, Sweden|
|Publication type:||Abstract and Fulltext|
|Abstract:||Golan et al. (1996) proposed an alternative method for the parameters estimation of the regression models, in case of ill-posed problems, as an extension of the entropy measure, introduced by Shannon and as a generalization of the Maximum Entropy Principle (MEP) developed by Jaynes (1957, 1968).|
The job satisfaction model is considered as motivating example, for developing a performance comparative study between the GME and PLS regression methods. Both GME and PLS methods are implemented on the job satisfaction model, where several level of correlation are generated among the predictors. For each level of correlation, regression coefficients and diagnostic values are calculated, for showing the performance of both methods in case of ill-posed problems.
The paper is divided in two main sections: The first part consider the introduction of the two estimation methods, in way to give a general overview of both techniques and also the main characteristics.
The second part gives a brief introduction to the job satisfaction model and then starts with the simulation study, comparing both estimation results, giving a discussion in case of multicollinearity problem.
|Keywords:||Generalized Maximum Entropy, Partial Least Squares, Job Satisfaction, Multicollinearity, Bootstrap|
|No. of pages:||8|
|Series:||Linköping Electronic Conference Proceedings|
|Publisher:||Linköping University Electronic Press, Linköpings universitet|
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