Section: New Results

Modern methods of data analysis

Participants: R. Bar, B. Lalloué, J-M. Monnez, C. Padilla, D. Zmirou, S. Deguen.

In 2012, our contributions to data analysis in a Biological context are twofold:

• At a theoretical level, we have kept on working on the so-called online data analysis alluded to at the Scientific Foundations Section. Specifically we have carried on in [15] (see also [4] ) the analysis of data whose characteristics such as mathematical expectation or covariance matrix may vary with time, a problem which arises very naturally in this context. Moreover, in order to save computation time and thus take into account more data, a method considering several data at each step (we talk about data blocks) is proposed. This technique can also be useful if data are sent and received block-wise. In parallel, a R package performing most of the methods of factorial analysis in an online way is under development.

• At a practical level, our efforts have focused (cf. [19] ) on an interesting study concerning the construction of a socio-economic neighborhood index which might quantify health inequalities. While several socio-economic indices already exist in this application field, most of them are very simple both in term of methodological construction and of number of variables taken into account, and only a few use data mining techniques. In order to exploit the large data sets of socio-economic variables provided by censuses and create neighborhood socio-economic indices yielding a better highlight of social health inequalities, a procedure was set in order to automatically select the best indicators in a set of socio-economic variables and synthesize them in a quantitative index. Application to three French metropolitan areas allowed testing the procedure and confirming both its reproducibility on various urban areas and the quality of the neighborhood socio-economic indices we had created (according to field experts and study partners). In this context, our expertise in data analysis allows for a good prediction by means of rigorous methods. Eventually, in order to simplify the application of the creation procedure of a socio-economic index for non-statisticians, a R package called SesIndexCreatoR was created to implement it.

• Publication of the sharp results obtained in [8] on local regression techniques.