| Abstract: | By extending a technique for testing the difference between two dependent correlations developed by Wolfe, a strategy is proposed in a more general matrix context for evaluating a variety of data analysis schemes that are supposed to clarify the structure underlying a set of proximity measures. In the applications considered, a data analysis scheme is assumed to reconstruct in matrix form the given data set (represented as a proximity matrix) based on some specific model or procedure. Thus, an evaluation of the adequacy of reconstruction can be developed by comparing matrices, one containing the original proximities and the second containing the reconstructed values. Possible applications in multidimensional scaling, clustering, and related contexts are emphasized using four broad categories: (a) Given two different reconstructions based on a single data set, does either represent the data significantly better than the other? (b) Given two reconstructions based on a single data set using two different procedures (or possibly, two distinct data sets and a common method), is either reconstruction significantly closer to a particular theoretical structure that is assumed to underlie the data (where the latter is also represented in matrix form)? (c) Given two theoretical structures and one reconstruction based on a single data set, does either represent the reconstruction better than the other? (d) Given a single reconstruction based on one data set, is the information present in the data accounted for satisfactorily by the reconstruction? In all cases, these tasks can be approached by a nonparametric procedure that assesses the similarity in pattern between two appropriately defined matrices. The latter are obtained from the original data, the reconstructions, and/or the theoretical structures. Finally, two numerical examples are given to illustrate the more general discussion. |
