Analysis of local sensitivity to nonignorability with missing outcomes and predictors

Biometrics ◽  
2021 ◽  
Author(s):  
Heng Chen ◽  
Daniel F. Heitjan
Keyword(s):  
Local Sensitivity

scholarly journals Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling

2013 ◽  
Vol 17 (2) ◽  
pp. 461-478 ◽  
Author(s):  
L. Loosvelt ◽  
H. Vernieuwe ◽  
V. R. N. Pauwels ◽  
B. De Baets ◽  
N. E. C. Verhoest
Keyword(s):  
Sensitivity Analysis ◽  
Soil Texture ◽  
Compositional Data ◽  
High Sensitivity ◽  
Hydrologic Model ◽  
Model Output ◽  
Local Sensitivity ◽  
Hydrologic Modelling ◽  
Compositional Model ◽  
Additional Data Collection

Abstract. Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moistureretention curve.

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