Experimental Data Modeling: Issues in Empirical Identification of Distribution

Identification of distribution underlying experimental data sets, lacking established mechanistic models, calls for an inferential process involving necessarily uncertainty of some sort. While components pertaining to parameter estimation are routinely tak en into account, those related to selection of distribution form often are not; their awkward theoretical evaluation may explain why the issue tends to be conveniently ignored. Such an attitude may however lead to severe underestimation of overall uncertainty, s ince the component accounting for identification of distribution form often exceeds those concerning estimates of parameters. A pragmatic approach is presented, relying upon numerical simulation, allowing realistic evaluation of uncertainty inherent in empirical identification of form in a straightforward way. Application to an actual case is presented, and issues concerning identification procedure for parameters of auxiliary empirical distributions are discussed.