Revision as of 04:40, 18 November 2014 edit Cmallon (talk \| contribs) 10 edits m Corrected equation for the sequence yielded in steps of "delta". The correct trajectory of each input reads {0, 1/(p-1), 2/(p-1), ..., 1}. This essentially keeps the denominator constant, therefore leading to 1 eventually. This yields p "levels". ← Previous edit		Revision as of 17:10, 14 March 2016 edit undo Louder noises (talk \| contribs) 1 edit m →Methodology: Removed questioning of the improvement and the source of Campolongo. This paper presents the method, it is a straight forward improvement which prevents negative data from skewing the mean. Tag: Visual edit Next edit →
Line 38: These two measures need to be read together (e.g. on a two-dimensional graph) in order to rank input factors in order of importance and identify those inputs which do not influence the output variability. Low values of both <math> \mu </math> and <math> \sigma </math> correspond to a non-influent input. An improvement~~{{Citation needed\|date=January 2010}}~~ of this method was developed by Campolongo et al.<ref>Campolongo, F., J. Cariboni, and A. Saltelli (2007). An effective screening design for sensitivity analysis of large models. ''Environmental Modelling and Software'', '''22''', 1509–1518.</ref>~~{{Better source\|date=January 2010}}~~ who proposed a revised measure <math> \mu^* </math>, which on its own is sufficient to provide a reliable ranking of the input factors. The revised measure is the mean of the distribution of the absolute values of the elementary effects of the input factors:<br /> : <math> \mu_i^* = \frac{1}{r} \sum_{j=1}^r \left\| d_i \left( X^{(j)} \right) \right\| </math>. The use of <math> \mu^* </math> solves the problem of the effects of opposite signs which occurs when the model is non-monotonic and which can cancel each other out, thus resulting in a low value for <math> \mu </math>.

Elementary effects method: Difference between revisions