换话题 发表于 2025-3-25 05:17:04
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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祝贺 发表于 2025-3-25 15:30:20
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 iGuileless 发表于 2025-3-25 18:03:01
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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 conditironic 发表于 2025-3-26 00:54:18
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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.insurgent 发表于 2025-3-26 09:56:44
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 likesacrum 发表于 2025-3-26 14:01:36
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.Statins 发表于 2025-3-26 20:42:11
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