限制性重权分配:一种最佳的运输方法
Abhisek Chakraborty1, Anirban Bhattacharya1, Debdeep Pati1
1Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
Entropy (Basel, Switzerland)
|March 28, 2024
概括
这项研究引入了一种灵活的数据重权方法,使用最大和最佳运输. 它确保调整后的数据分布符合约束,同时保持接近目标分布,有助于各种统计任务.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 优化优化 优化优化
背景情况:
- 经验分布权重对于统计分析至关重要.
- 现有的方法在处理复杂的约束方面存在局限性.
研究的目的:
- 为重量调整的实证分布开发一个灵活的框架.
- 采用最佳运输方法来纳入非参数分布约束.
主要方法:
- 利用最大的原则.
- 利用最佳的运输指标来实现分布式接近.
- 为受约束数据重权衡制定一个一般框架.
主要成果:
- 引入了一种新的方法,以提高重量调整分布的灵活性.
- 证明了对投资组合分配,复杂调查和算法公平性的适用性.
- 该方法允许微妙偏离预先规定的分布.
结论:
- 拟议的框架提供了比经验概率更大的灵活性.
- 它有效地处理参数分布引导的约束.
- 这种方法在各种统计和机器学习应用中具有多样性.
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