Statistics sensitivity

Examples of mathematical methods include nominal range sensitivity analysis (Cullen Frey, 1999) and differential sensitivity analysis (Hwang et al., 1997 Isukapalli et al., 2000). Examples of statistical sensitivity analysis methods include sample (Pearson) and rank (Spearman) correlation analysis (Edwards, 1976), sample and rank regression analysis (Iman Conover, 1979), analysis of variance (Neter et al., 1996), classification and regression tree (Breiman et al., 1984), response surface method (Khuri Cornell, 1987), Fourier amplitude sensitivity test (FAST) (Saltelli et al., 2000), mutual information index (Jelinek, 1970) and Sobol s indices (Sobol, 1993). Examples of graphical sensitivity analysis methods include scatter plots (Kleijnen Helton, 1999) and conditional sensitivity analysis (Frey et al., 2003). Further discussion of these methods is provided in Frey Patil (2002) and Frey et al. (2003, 2004). 

Sensitivity analysis methods can be used in combination with methods for variance propagation. For example, Cullen Frey (1999) describe how variance in the sum of random numbers can be apportioned among the inputs to the sum. All of the statistical sensitivity methods mentioned above can be applied to the results of Monte Carlo simulation, in which... 

Determination of Assay Sensitivity with Replication and Statistics. Sensitivity can be defined as the ability of a test to discriminate between adjacent levels or concentrations of test analyte. There are other definitions of sensitivity, but the one specified is sufficiently general to serve several needs in residue analysis. For example, the definition recognizes that test sensitivity can vary with the point on the standard curve. If one of the points used is zero, then the sensitivity estimate can be either the level of smallest quantitation or the level of detectability of the method. The... 

As one attempts to assign risks that are acceptable to humans (1 in 10 or 10 ), it is clear that an enonnous sample size is needed. Actual statistical sensitivity of the toxicology tests is dependent upon the number of animals used, the background incidence of the tumors seen and the doses administered. 

Freak wave research has two basic objectives. The first is an understanding of the mechanisms that create freak waves severaf mechanisms have been proposed to explain why extreme-wave events occur in the ocean. The second is to estabhsh a reliable statistical model for the occurrence of freak waves. However, the problem is difficult because a freak wave is only one wave, or perhaps just a few waves, in a wave train. Therefore, its occurrence exhibits statistical sensitivity. Moreover, an extreme wave event is transient it forms and disappears quickly in both time and space. To address these problems, we should study wave populations that contain freak waves, rather than concentrate on the features of an individual wave profile. (This is not to say, however, that observations of single events are unimportant.) Once such wave populations can be characterized, then it should be possible to estimate how often waves of any given size will occur. 

Greene, T., Emhart, C.B., 1993. Dentine lead and intelligence prior to school entry a statistical sensitivity analysis. J. Clin. Epidemiol. 46, 323—339. 

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