Morris method

In applied statistics, the Morris method for global sensitivity analysis is a so-called one-step-at-a-time method (OAT), 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 of local changes at different points x(1 → r) of the possible range of input values. The method starts by sampling a set of start values within the defined ranges of possible values for all input variables and calculating the subsequent model outcome. The second step changes the values for one variable (all other inputs remaining at their start values) and calculates the resulting change in model outcome compared to the first run. Next, the values for another variable are changed (the previous variable is kept at its changed value and all other ones kept at their start values) and the resulting change in model outcome compared to the second run is calculated. This goes on until all input variables are changed. This procedure is repeated r times (where r is usually taken between 5 and 15), each time with a different set of start values, which leads to a number of r(k + 1) runs, where k is the number of input variables. Such number is very efficient compared to more demanding methods for sensitivity analysis.^[1]

A sensitivity analysis method widely used to screen factors in models of large dimensionality is the design proposed by Morris.^[2] The Morris method deals efficiently with models containing hundreds of input factors without relying on strict assumptions about the model, such as for instance additivity or monotonicity of the model input-output relationship.The Morris method is simple to understand and implement, and its results are easily interpreted. Furthermore it is economic in the sense that it requires a number of model evaluations that is linear in the number of model factors. The method can be regarded as global as the final measure is obtained by averaging a number of local measures (the elementary effects), computed at different points of the input space.^[3]

See also

• Monte Carlo method

Notes

1. ^ Campolongo 2003
2. ^ Morris 1991
3. ^ Sensitivity analysis 2003^{[Full citation needed]}

References

• Campolongo, F., S. Tarantola and A. Saltelli. (1999). "Tackling quantitatively large dimensionality problems.". Computer Physics Communication 1999: 75–85. http://www.sciencedirect.com/science/article/pii/S0010465598001659. 
• Morris, M.D. (1991). "Factorial Sampling Plans for Preliminary Computational Experiments". Technometrics 33: 161–174. http://www.abe.ufl.edu/Faculty/jjones/ABE_5646/2010/Morris.1991%20SA%20paper.pdf. 
• Campolongo,Cariboni,Saltelli, F., J.and A. (2003). Sensitivity analysis: the Morris method versus the variance based measures. Submitted to Technometrics. http://library.lanl.gov/cgi-bin/getdoc?event=SAMO2004&document=samo04-52.pdf. 

External links

Morris method paper