Mishra, SK (2012): A note on construction of heuristically optimal Pena’s synthetic indicators by the particle swarm method of global optimization.
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Abstract
Pena’s method of construction of a synthetic indicator is very sensitive to the order in which the constituent variables (whose linear aggregation yields the synthetic indicator) are arranged. Due to this, Pena’s method can at present give only an arbitrary synthetic indicator whose representativeness is indeterminate and uncertain, especially when the number of constituent variables is not very small. This paper uses discrete global optimization method based on the Particle Swarms to obtain a heuristically optimal order in which the constituent variables can be arranged so as to yield Pena’s synthetic indicator that maximizes the minimal absolute (or squared) correlation with its constituent variables.
Item Type: | MPRA Paper |
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Original Title: | A note on construction of heuristically optimal Pena’s synthetic indicators by the particle swarm method of global optimization |
Language: | English |
Keywords: | Synthetic indicators, Pena’s distance, Particle swarm, Discrete Global Optimization, Composite indices, Maxi-min absolute correlation |
Subjects: | C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C43 - Index Numbers and Aggregation C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C44 - Operations Research ; Statistical Decision Theory C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis |
Item ID: | 37625 |
Depositing User: | Sudhanshu Kumar Mishra |
Date Deposited: | 25 Mar 2012 02:28 |
Last Modified: | 01 Oct 2019 18:12 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/37625 |