模拟研究,以评估何时等离子模拟优于参数模拟,以估计线性回归中最小平方估计器的平均平方误差
Marieke Stolte1, Nicholas Schreck2, Alla Slynko3
1Department of Statistics, TU Dortmund University, Dortmund, North Rhine-Westphalia, Germany.
参数和等离子模拟之间的选择取决于研究的具体情况. 当真正的数据生成过程已知时,参数模拟是最好的,而等离子模式模拟提供了更现实的数据,但依赖于结果生成模型的假设.
科学领域:
- 统计 统计 统计 统计
- 计算方法 计算方法
背景情况:
- 模拟研究对于评估统计方法至关重要.
- 参数和等离子模拟是常见的方法,每种模拟都有不同的数据生成策略.
- 设计公平的模拟研究对于方法开发和选择至关重要.
研究的目的:
- 为了比较参数和等离子模拟的性能,以在线性回归中估计最小平方估计器 (LSE) 的平均平方误差 (MSE).
- 调查与真实数据生成过程 (DGP) 和结果生成模型 (OGM) 的偏差如何影响模拟性能.
- 为了确定何时每个模拟类型是可取的.
主要方法:
- 参数模拟 (使用伪随机数) 和等离子模拟 (重新采样真实特征并生成结果) 的比较.
- 专注于用线性回归模型估计LSE的MSE.
- 对DGP和OGM假设差异对模拟结果的影响分析.
主要成果:
- 参数和等离子模拟之间的选择受诸如特征的数量和假设偏离真实DGP的程度等因素的影响.
- 在Plasmode模拟中的重新采样策略,特别是小比例的次采样,会显著影响结果.
- 当真正的DGP和OGM已知时,参数模拟是最佳的,这在实践中很少发生.
结论:
- 两种模拟方法都不是普遍优越的;最佳选择取决于具体的研究条件和所作假设的性质.
- 等离子模式模拟,特别是适当的重新采样,可以在真正的DGP未知时提供更现实的评估.
- 研究人员必须仔细考虑在参数和等离子模拟设计中与假设制造相关的权衡和潜在偏差.
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