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Trindade, G. & Ambrósio, J (2012). An optimization method to estimate models with store-level data: a case study. European Journal of Operational Research. 217 (3), 664-672
Export Reference (IEEE) 
G. M. Trindade and A. J,  "An optimization method to estimate models with store-level data: a case study", in European Journal of Operational Research, vol. 217, no. 3, pp. 664-672, 2012
Export BibTeX 
@article{trindade2012_1716240604279,
	author = "Trindade, G. and Ambrósio, J",
	title = "An optimization method to estimate models with store-level data: a case study",
	journal = "European Journal of Operational Research",
	year = "2012",
	volume = "217",
	number = "3",
	doi = "10.1016/j.ejor.2011.08.032",
	pages = "664-672",
	url = "http://www.sciencedirect.com/science/article/pii/S0377221711008034"
}
Export RIS 
TY  - JOUR
TI  - An optimization method to estimate models with store-level data: a case study
T2  - European Journal of Operational Research
VL  - 217
IS  - 3
AU  - Trindade, G.
AU  - Ambrósio, J
PY  - 2012
SP  - 664-672
SN  - 0377-2217
DO  - 10.1016/j.ejor.2011.08.032
UR  - http://www.sciencedirect.com/science/article/pii/S0377221711008034
AB  - The quality of the estimation of a latent segment model when only store-level aggregate data is available seems to be dependent on the computational methods selected and in particular on the optimization methodology used to obtain it. Following the stream of work that emphasizes the estimation of a segmentation structure with aggregate data, this work proposes an optimization method, among the deterministic optimization methods, that can provide estimates for segment characteristics as well as size, brand/product preferences and sensitivity to price and price promotion variation estimates that can be accommodated in dynamic models. It is shown that, among the gradient based optimization methods that were tested, the Sequential Quadratic Programming method (SQP) is the only that, for all scenarios tested for this type of problem, guarantees of reliability, precision and efficiency being robust, i.e., always able to deliver a solution. Therefore, the latent segment models can be estimated using the SQP method when only aggregate market data is available.
ER  -