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The information detection for the generalized additive model
Journal of Statistical Computation and Simulation ( IF 1.2 ) Pub Date : 2020-06-09 , DOI: 10.1080/00949655.2020.1774883
San-Teng Huang, Wei-Ying Wu

ABSTRACT Many non-linear models such as the additive models or varying models are often used to fit the complex data. However, how to select a simplified model in the prediction problem or data interpretation is necessary and challenged. In this work, the concerned regression model consists of many unknown group regressor functions, and some of them can be irrelevant for the response variable. To find an adequate and simplified model, an algorithm is developed to search the important regressor functions and their related structures through the introduction of basis functions with the Lasso-type penalized scheme. The performance of the proposed algorithm is evaluated under simulation studies and real data analyses.

中文翻译:

广义可加模型的信息检测

摘要 许多非线性模型,如加法模型或变化模型,经常用于拟合复杂数据。然而,如何在预测问题或数据解释中选择一个简化的模型是必要和挑战的。在这项工作中,相关的回归模型由许多未知的组回归函数组成,其中一些可能与响应变量无关。为了找到一个合适的简化模型,开发了一种算法,通过引入具有套索型惩罚方案的基函数来搜索重要的回归函数及其相关结构。所提出算法的性能在模拟研究和真实数据分析下进行评估。
更新日期:2020-06-09
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