Abstract
We give an overview on several semiparametric methods utilised to model high-dimensional data. Our approach is semiparametric in nature and is related to Generalised Linear Models. We focus on dynamic estimation techniques in this setting. In particular we discuss Generalized Additive Models (GAM), Alternating Conditional Expectations (ACE), Average Derivative Estimation (ADE), semiparametric weighted least squares (Single Index Models, SIM), Projection Pursuit Regression (PPR) , and Sliced Inverse Regression (SIR). Their performance in practice and theory is compared.
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Härdle, W.K., Turlach, B.A. (1993). Semiparametric Approaches to Dimension Reduction. In: Karmann, A., Mosler, K., Schader, M., Uebe, G. (eds) Operations Research ’92. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-12629-5_101
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DOI: https://doi.org/10.1007/978-3-662-12629-5_101
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