论文标题
广义添加性部分线性模型的统计推断
Statistical Inference for Generalized Additive Partially Linear Model
论文作者
论文摘要
广义添加剂模型（GAM）是一种功能强大的工具，并且已经对其进行了充分的研究。该模型类有助于识别加法回归结构。通过可用的测试过程，如果某些组件功能具有参数形式，则可以识别回归结构甚至更锋利。广义加性部分线性模型（GAPLM）享有GLM的简单性和GAM的灵活性，因为它们结合了参数和非参数组件。我们使用混合样条折叠式内核估计方法，该方法结合了样条和核方法的最佳特征，以在α混合条件下快速，高效和可靠的估计进行快速，高效和可靠的估计。此外，在独立条件下提供了用于测试参数的总体趋势和经验可能性置信区域的同时置信走廊（SCC）。获得渐近特性，模拟结果支持理论特性。对于该应用程序，我们使用GAPLM来提高19610美元的德国公司的默认预测的准确性比率。本文的定量可在https://github.com上找到。
The Generalized Additive Model (GAM) is a powerful tool and has been well studied. This model class helps to identify additive regression structure. Via available test procedures one may identify the regression structure even sharper if some component functions have parametric form. The Generalized Additive Partially Linear Models (GAPLM) enjoy the simplicity of the GLM and the flexibility of the GAM because they combine both parametric and nonparametric components. We use the hybrid spline-backfitted kernel estimation method, which combines the best features of both spline and kernel methods for making fast, efficient and reliable estimation under alpha-mixing condition. In addition, simultaneous confidence corridors (SCCs) for testing overall trends and empirical likelihood confidence region for parameters are provided under independent condition. The asymptotic properties are obtained and simulation results support the theoretical properties. For the application, we use the GAPLM to improve the accuracy ratio of the default predictions for $19610$ German companies. The quantlets for this paper are available on https://github.com.
