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• W2116778993 abstract "The purpose of this paper is to empirically compare the out-of-sample predictive capabilities of artificial neural networks, logit and probit models using dichotmous choice contingent valuation survey data. The authors find that feed-forward backpropagation artificial neural networks perform relatively better than the binary logit and probit models with linear index functions. In addition, guidelines for modeling contingent valuation survey data and how to estimate median WTP using artificial neural networks are provided." @default.
• W2116778993 created "2016-06-24" @default.
• W2116778993 creator A5012319983 @default.
• W2116778993 creator A5049383935 @default.
• W2116778993 date "2003-01-01" @default.
• W2116778993 modified "2023-09-27" @default.
• W2116778993 title "Networking Your Way to a Better Prediction: Effectively Modeling Contingent Valuation Survey Data" @default.
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• W2116778993 hasPublicationYear "2003" @default.
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