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The impact of statistical models on scalings derived from multi-machine H-mode threshold experiments

机译：统计模型对多机H模式阈值实验得出的标度的影响

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摘要

The predicted H-mode power threshold, PL-H, for ITER is generally estimated from the international global H-mode threshold database (IGDBTH) by ordinary least squares log-linear (OLS) regressions. Such fits assume that errors are uncorrelated and (i) errors in PL-H are much greater than those in the other parameters, (ii) errors are normally distributed and (iii) relative errors are equal for all experiments. In this paper, the validity of this statistical model for the IGDBTH is examined, by use of the more generalized maximum-likelihood method. Results indicate that all three assumptions bias the resulting scaling and so need to be relaxed. A fit relaxing all three constraints lies outside the error bars of the OLS, indicating that the choice of the statistical model makes a significant contribution to the resulting scaling. A chi-squared analysis shows that none of the studied models are entirely consistent with the data, indicating that further refinement of the physical and statistical model is required. For ITER-like parameters, a maximum-likelihood analysis shows a predicted threshold of 38.4 MW, compared with 31.1 MW for the OLS, indicating that OLS tends to under predict and that quoted confidence intervals tend to be too small. However, further studies of the sources of errors in the IGDBTH would be required before estimates based on more detailed statistical models can be given with confidence.
机译：通常，通过普通最小二乘对数线性（OLS）回归从国际全局H模式阈值数据库（IGDBTH）估算ITER的预测H模式功率阈值PL-H。这种拟合假设误差是不相关的，并且（i）PL-H中的误差远大于其他参数中的误差，（ii）误差是正态分布的，并且（iii）所有实验的相对误差均相等。在本文中，通过使用更广义的最大似然法来检验该统计模型对IGDBTH的有效性。结果表明，所有三个假设均会偏向最终的缩放比例，因此需要放宽。放宽所有三个约束的拟合位于OLS的误差线之外，表明统计模型的选择对结果缩放有重要贡献。卡方分析表明，没有一个研究模型与数据完全一致，这表明需要进一步完善物理和统计模型。对于类似ITER的参数，最大似然分析显示的预测阈值为38.4 MW，而OLS的预测阈值为31.1 MW，这表明OLS倾向于低于预测，引用的置信区间倾向于太小。但是，在可以放心地基于更详细的统计模型进行估计之前，需要进一步研究IGDBTH中的错误来源。

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