Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households
A new logistic regression algorithm based on evolutionary product-unit (PU) neural networks is used in this paper to determine the assets that influence the decision of poor households with respect to the cultivation of non-traditional crops (NTC) in the Guatemalan Highlands. In order to evaluate high-order covariate interactions, PUs were considered to be independent variables in product-unit neural networks (PUNN) analysing two different models either including the initial covariates (logistic regression by the product-unit and initial covariate model) or not (logistic regression by the product-unit model). Our results were compared with those obtained using a standard logistic regression model and allow us to interpret the most relevant household assets and their complex interactions when adopting NTC, in order to aid in the design of rural policies.
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- von Braun, Joachim & Hotchkiss, David & Immink, Maarten D. C., 1989. "Nontraditional export crops in Guatemala: effects on production, consumption, and nutrition," Research reports 73, International Food Policy Research Institute (IFPRI).
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- Carletto, Calogero & de Janvry, Alain & Sadoulet, Elisabeth, 1999. "Sustainability in the Diffusion of Innovations: Smallholder Nontraditional Agro-Exports in Guatemala," Economic Development and Cultural Change, University of Chicago Press, vol. 47(2), pages 345-69, January.
- Cook, Deborah F. & Zobel, Christopher W. & Wolfe, Mary Leigh, 2006. "Environmental statistical process control using an augmented neural network classification approach," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1631-1642, November.
- Immink, Maarten D C & Alarcon, Jorge A, 1993. "Household Income, Food Availability, and Commercial Crop Production by Smallholder Farmers in the Western Highlands of Guatemala," Economic Development and Cultural Change, University of Chicago Press, vol. 41(2), pages 319-42, January.
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