适应性LASSO估计用于功能隐藏的动态地理统计模型.
Paolo Maranzano1,2, Philipp Otto3, Alessandro Fassò4
1Department of Economics, Management and Statistics (DEMS), University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano, Italy.
概括
我们为功能隐藏动态地缘统计模型 (f-HDGM) 开发了一种新算法,可以有效地选择重要变量和功能组件. 这种方法简化了模型并提高了预测准确性,特别是当使用一个标准错误规则时.
科学领域:
- 地质统计学 在地质统计学
- 统计建模 统计建模
- 功能数据分析功能数据分析
背景情况:
- 地理统计模型对于分析空间引用数据至关重要.
- 功能数据分析扩展了传统方法来处理具有功能特征的数据.
- 隐藏的动态地质统计模型包括时间动态和未观察到的状态.
研究的目的:
- 为功能隐藏动态地理统计模型 (f-HDGM) 引入一种新的模型选择算法.
- 在f-HDGM中同时选择相关的支线基函数和固定效应的回归器.
- 为了自动处理与无关的功能系数或与非显著回归相关的整个函数.
主要方法:
- 该算法使用处罚的最大概率估计器 (PMLE) 与自适应的 LASSO 处罚.
- 对处罚的权重来自未被处罚的f-HDGM最大概率估计.
- 计算效率是通过对日志概率函数的局部二次近似来实现的.
主要成果:
- 蒙特卡洛模拟证明了算法的有效性在各种空间时间依赖下预测和参数估计.
- 应用于气候和土地覆盖共同变量的空气质量数据验证了算法的行为和可扩展性.
- 处罚估计显示预测能力相当于最大概率估计.
结论:
- 拟议的算法为f-HDGMs中的模型选择提供了一个强大的方法.
- 使用一个标准错误规则会导致更准确,更简单,更易于解释的模型.
- 该算法为复杂的地理统计建模问题提供了计算高效和可扩展的解决方案.
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