Multivariate methods for the analysis of complex and big data in forensic sciences. Application to age estimation in living persons

摘要

Researchers handle increasingly higher dimensional datasets, with many variables to explore. Such datasets pose several problems, since they are difficult to handle and present unexpected features. As dimensionality increases, classical statistical analysis becomes inoperative. Variables can present redundancy, and the reduction of dataset dimensionality to its lowest possible value is often needed. Principal components analysis (PCA) has proven useful to reduce dimensionality but present several shortcomings. As others, forensic sciences will face the issues specific related to an evergrowing quantity of data to be integrated. Age estimation in living persons, an unsolved problemso far, could benefit from the integration of various sources of data, e.g., clinical, dental and radiological data.