s and optimization applied to parameter estimation under uncertainty
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ID: 154843
2018
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Abstract
We present a methodology through exemplification to perform parameter estimation subject to possible factors of uncertainty. The underlying optimization problem is posed in the framework of the theory of interval-valued optimization. The implementation of numerical procedures required to achieve efficient solutions implied the use of the $\ell_1$ norm instead of usual $\ell_2$ regression. Finally, an implementation using real data was performed, demonstrating the ability of interval analysis to encapsulate uncertainty while facing non-trivial parameter estimation problems.
| Reference Key |
gallego-posada2018boletims
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|---|---|
| Authors | ;Jose Daniel Gallego-Posada;Maria Eugenia Puerta-Yepes |
| Journal | urban geography |
| Year | 2018 |
| DOI |
10.5269/bspm.v36i2.29309
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