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 well-established usage of Benfordness statistical testing lies within investigations aimed to ascertain if balance sheet and income statement data are genuine. A typical, frustrating problem of Benfordness statistical tests on big, practical datasets is that they often provide p-valuessmaller than expected when the Benfordness null hypothesis is very realistic. A possible reason is that data are contaminated by some kind of noise. In this paper we propose the deconvolution approach to alleviate this issue, using both simulated and real data.
Validating Benfordness on contaminated data
Di Marzio, Marco;Fensore, Stefania;Passamonti, Chiara
2024-01-01
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 well-established usage of Benfordness statistical testing lies within investigations aimed to ascertain if balance sheet and income statement data are genuine. A typical, frustrating problem of Benfordness statistical tests on big, practical datasets is that they often provide p-valuessmaller than expected when the Benfordness null hypothesis is very realistic. A possible reason is that data are contaminated by some kind of noise. In this paper we propose the deconvolution approach to alleviate this issue, using both simulated and real data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
