B0780
Title: On detecting data Benfordness
Authors: Marco Di Marzio - University of Chieti-Pescara (Italy)
Stefania Fensore - University of Chieti-Pescara (Italy)
Chiara Passamonti - University of Chieti-Pescara (Italy) [presenting]
Abstract: Benford's Law is a mathematical model, very recurrent in practice for a wide variety of datasets, used to represent the frequencies of digits. A typical, frustrating problem of Benfordness statistical tests is that they often provide p-values smaller than expected, even if the Benfordness null hypothesis is accepted as true. A possible reason is that data are affected by some kind of noise. A deconvolution technique able to alleviate this issue is proposed.
