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In applied statistics, the Morris method for global sensitivity analysis is a so-called one-factor-at-a-time method, meaning that in each run only one input parameter is given a new value. It facilitates a global sensitivity analysis by making a number r {\displaystyle r} of local changes at different points x ( 1 → r ) {\displaystyle x(1 ...
Regression analysis, in the context of sensitivity analysis, involves fitting a linear regression to the model response and using standardized regression coefficients as direct measures of sensitivity. The regression is required to be linear with respect to the data (i.e. a hyperplane, hence with no quadratic terms, etc., as regressors) because ...
Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. [1] [2] Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs.
In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. RSM is an empirical model which employs the use of mathematical and statistical techniques to relate input variables, otherwise known as factors, to the response.
Sensitivity analysis studies the relation between the uncertainty in a model-based the inference [clarify] and the uncertainties in the model assumptions. [ 1 ] [ 2 ] Sensitivity analysis can play an important role in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study. [ 3 ]
One can use sensitivity indices (see variance-based sensitivity analysis) to define the most influential variables for decomposition or choose them manually according to the decision-problem context (for example, only those input variables that the decision-maker can act upon). Two to three input variables, ordered by decreasing value of their ...
That is, one can seek to understand what observations (measurements of dependent variables) are most and least important to model inputs (parameters representing system characteristics or excitation), what model inputs are most and least important to predictions or forecasts, and what observations are most and least important to the predictions ...
Sensitivity analysis studies the relationship between the output of a model and its input variables or assumptions. Historically, the need for a role of sensitivity analysis in modelling, and many applications of sensitivity analysis have originated from environmental science and ecology. [1]