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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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