Titlebook: Nonparametric and Semiparametric Models; Wolfgang H?rdle,Axel Werwatz,Stefan Sperlich Book 2004 Springer-Verlag Berlin Heidelberg 2004 Add

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Single Index Modelseginning of Part II, the SIM is one possibility for generalizing the GLM or for restricting the multidimensional regression .(.|.) to overcome the curse of dimensionality and the lack of interpretability. For more examples of motivating the SIM see Ichimura (1993). Among others, this reference menti

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Generalized Partial Linear Modelsinite dimensional parameter and .(●) a smooth function. Here, we assume again a decomposition of the explanatory variables into two vectors, . and .. The vector . denotes a .-variate random vector which typically covers categorical explanatory variables or variables that are known to influence the i

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0172-7397 ure. The book considers high dimensional objects, as density functions and regression. The semiparametric modeling technique compromises the two aims, flexibility and simplicity of statistical procedures, by introducing partial parametric components. These components allow to match structural condit

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Single Index Modelsse of dimensionality and the lack of interpretability. For more examples of motivating the SIM see Ichimura (1993). Among others, this reference mentions duration, truncated regression (Tobit) and errors-in-variables modeling.

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Book 2004ook considers high dimensional objects, as density functions and regression. The semiparametric modeling technique compromises the two aims, flexibility and simplicity of statistical procedures, by introducing partial parametric components. These components allow to match structural conditions like

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Generalized Partial Linear Modelsndex in a linear way. The vector . is a .-variate random vector of continuous explanatory variables which is to be modeled in a nonparametric way. Economic theory or intuition should guide you as to which regressors should be included in . or ., respectively.

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