对于高维通用线性模型的最佳波松子样本取样脱相关得分
1School of Statistics and Data Science, KLMDASR, LEBPS and LPMC, Nankai University, Tianjin, People's Republic of China.
Journal of applied statistics
|October 23, 2024
概括
本研究介绍了高维通用线性模型 (GLM) 的最佳Poisson子样本采集方法,以改善参数估计和推断. 拟议的技术提高了效率,并减轻了大型数据集的问题.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 高维通用线性模型 (GLMs) 面临着大规模数据集的挑战.
- 有效的估计和推断对于这些模型至关重要.
研究的目的:
- 为高维的GLMs开发一个统一的最优的Poisson亚抽样方案.
- 改进低维参数分区的估计和推断.
主要方法:
- 提出了一个Poisson亚抽样脱关联得分函数.
- 部分样本估计器的一致性和异常正常性已被证明.
- 制定了一个一般的最佳分样标准 (A-和L-最佳性).
主要成果:
- 该方法减轻了不准确的干扰参数估计的影响.
- 亚样本估计器显示一致性和非对称的正常性.
- 通过最佳亚抽样标准来提高估计效率.
结论:
- 拟议的Poisson亚抽样方案为大规模的GLM提供了一种有效的方法.
- 该方法提供了理论上的保证和实际实施策略.
- 模拟研究和现实世界的数据证实了该方法的令人满意的性能.
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