Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households
AbstractA 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 195 (2009)
Issue (Month): 2 (June)
Contact details of provider:
Web page: http://www.elsevier.com/locate/eor
Neural networks Logistic regression Product-unit Evolutionary algorithms Sustainability Poor households;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bose, Indranil & Pal, Raktim, 2006. "Predicting the survival or failure of click-and-mortar corporations: A knowledge discovery approach," European Journal of Operational Research, Elsevier, vol. 174(2), pages 959-982, October.
- 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.
- Octavio Damiani, 2000. "The State and Nontraditional Agricultural Exports in Latin America: Results and Lessons of Three Case Studies," IDB Publications 51478, Inter-American Development Bank.
- 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.
- Jorge Guardiola & Teresa Garcia-Muñoz, 2009. "Subjective well-being and basic needs: Evidence from rural Guatemala," ThE Papers 09/03, Department of Economic Theory and Economic History of the University of Granada..
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.