Influence diagnostics are used to identify data points that have a disproportionate impact on model parameters, performance and/or predictions, providing valuable information for use in model calibration. Regression-theory influence diagnostics identify influential data by combining the leverage and the standardised residuals, and are computationally more efficient than case-deletion approaches. This study evaluates the performance of a range of regression-theory influence diagnostics on ten case studies with a variety of model structures and inference scenarios including: nonlinear model response, heteroscedastic residual errors, data uncertainty and Bayesian priors. A new technique is developed, generalised Cook's distance, that is able to accurately identify the same influential data as standard case deletion approaches (Spearman rank correlation: 0.93–1.00) at a fraction of the computational cost (<1%). This is because generalised Cook's distance uses a generalised leverage formulation which outperforms linear and nonlinear leverage formulations due to less restrictive assumptions. Generalised Cook's distance has the potential to enable influential data to be efficiently identified on a wide variety of hydrological and environmental modelling problems.

影响力诊断用于识别对模型参数，性能和/或预测有不成比例的影响的数据点，从而为模型校准提供有价值的信息。回归理论影响诊断通过结合杠杆作用和标准化残差来识别有影响力的数据，并且在计算上比案例删除方法更有效。这项研究评估了对十种案例研究的一系列回归理论影响诊断的性能，这些案例研究具有各种模型结构和推理方案，包括：非线性模型响应，异方差残差，数据不确定性和贝叶斯先验。开发了一种新技术，将库克距离广义化，它能够以少量计算成本（<1％）准确识别出与标准案例删除方法相同的有影响力的数据（Spearman等级相关性：0.93–1.00）。这是因为广义库克距离使用的广义杠杆公式由于限制性较小的假设而优于线性和非线性杠杆公式。广义库克距离具有潜力，可以有效地识别各种水文和环境建模问题中的有影响力的数据。