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• W2183840339 abstract "We study additive models within the context of the parametric random field model proposed by Hamilton (2001). This is a flexible parametric approach to model nonlinearities in the context of a regression model. Though the model is parametric, it enjoys the flexibility of the nonparametric approach as it can approximate a large collection of nonlinear functions and it has the added advantage that there is no “curse of dimensionality”. If the true data generating process is additive, we propose a new specification of the random field model that is shown to have more accurate out of sample predictions than the Hamilton’s model. For a fixed sample and for k regressors, we approximate the individual contribution of each regressor to the conditional mean by a random field, such that the nonlinear part is the sum of k individual and independent random fields as opposed to the Hamilton’s model where one random field approximates the joint contribution of the k regressors. We" @default.
• W2183840339 created "2016-06-24" @default.
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• W2183840339 date "2004-01-01" @default.
• W2183840339 modified "2023-09-27" @default.
• W2183840339 title "The Additive Random Field Regression Model" @default.
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