论文标题
样本拟合可靠性
Sample Fit Reliability
论文作者
论文摘要
研究人员经常通过保持样本恒定并改变模型来测试和改善模型拟合。我们提出了通过保持模型恒定并改变样品来测试和改善样品拟合的方法。尽管引导程序是一种重新样本数据并估算模型中参数拟合的不确定性的众所周知的方法，我们将样本拟合可靠性（SFR）作为一组计算方法来重新置换数据并估算样品中观测值的拟合度的可靠性。 SFR使用评分来评估样品中每个观察结果的可靠性，退火以检查结果对消除不可靠数据的敏感性，并适合重新权威观察值，以进行更强大的分析。我们提供了模拟证据来证明使用SFR的优势，并复制了三项具有治疗效果的经验研究，以说明SFR如何揭示有关每项研究的新见解。
Researchers frequently test and improve model fit by holding a sample constant and varying the model. We propose methods to test and improve sample fit by holding a model constant and varying the sample. Much as the bootstrap is a well-known method to re-sample data and estimate the uncertainty of the fit of parameters in a model, we develop Sample Fit Reliability (SFR) as a set of computational methods to re-sample data and estimate the reliability of the fit of observations in a sample. SFR uses Scoring to assess the reliability of each observation in a sample, Annealing to check the sensitivity of results to removing unreliable data, and Fitting to re-weight observations for more robust analysis. We provide simulation evidence to demonstrate the advantages of using SFR, and we replicate three empirical studies with treatment effects to illustrate how SFR reveals new insights about each study.
