Feedforward Backpropagation Neural Networks in Prediction of Farmer Risk Preferences
用前馈反向传播神经网络和有序多项逻辑回归模型，预测堪萨斯农民对五个风险问题的回答，发现逻辑回归均方误差更小，但神经网络能更灵活地针对特定类别组合进行准确预测。
Abstract An out‐of‐sample prediction of Kansas farmers' responses to five surveyed questions involving risk is used to compare ordered multinomial logistic regression models with feedforward backpropagation neural network models. Although the logistic models often predict more accurately than the neural network models in a mean‐squared error sense, the neural network models are shown to be more accommodating of loss functions associated with a desire to predict certain combinations of categorical responses more accurately than others.