Varying Coefficient Regression Modeling using Deep Neural Network
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초록
Varying coefficient regression (VCR) models have garnered significant academic attention as important tools for modeling dynamic changes in regression coefficients in various fields of study. Several estimation methods have been developed for this regression model. In this paper, we propose a deep neural network model to fit VCR and analyze nonlinear relationships between responses and covariates. Empirical results demonstrate that the proposed method can be applied to nonlinear regression coefficients easily and effectively using high-dimensional vectors of smoothing variables. Overall, the proposed method outperforms existing alternatives.
Abstract
Varying coefficient regression (VCR) models have garnered significant academic attention as important tools for modeling dynamic changes in regression coefficients in various fields of study. Several estimation methods have been developed for this regression model. In this paper, we propose a deep neural network model to fit VCR and analyze nonlinear relationships between responses and covariates. Empirical results demonstrate that the proposed method can be applied to nonlinear regression coefficients easily and effectively using high-dimensional vectors of smoothing variables. Overall, the proposed method outperforms existing alternatives.
참고문헌 (15)
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