In this study we illustrate a statistical approach to questioned document examination. Specifically, we consider the construction of three classifiers that predict the writer of a sample document based on categorical data. To evaluate these classifiers, we use a data set with a large number of writers and a small number of writing samples per writer. Since the resulting classifiers were found to have near perfect accuracy using leave-one-out cross-validation, we propose a novel Bayesian-based cross-validation method for evaluating the classifiers.
"Construction and evaluation of classifiers for forensic document analysis." Ann. Appl. Stat. 5 (1) 381 - 399, March 2011. https://doi.org/10.1214/10-AOAS379