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• W2182776424 abstract "We study nonlinear models within the context of the flexible parametric random field regression model proposed by Hamilton (2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection of nonlinear functions and it has the added advantage that there is no “curse of dimensionality.”The fundamental premise of this paper is that the parametric random field model, though a good approximation to the true data generating process, is still a misspecified model. We study the asymptotic properties of the estimators of the parameters in the conditional mean and variance of a generalized additive random field regression under misspecification. � The notation follows Abadir and Magnus (2002). All software developed for this paper can be" @default.
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• W2182776424 date "2005-01-01" @default.
• W2182776424 modified "2023-09-25" @default.
• W2182776424 title "Statistical Inference and Prediction in Nonlinear Models using Additive Random Fields" @default.
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