On maximum entropy priors and a most likely likelihood in auditing
There are two basic questions auditors and accountants must consider when developing test and estimation applications using Bayes' Theorem: What prior probability function should be used and what likelihood function should be used. In this paper we propose to use a maximum entropy prior probabi...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 1998 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/4089 |
| Acceso en línea: | https://hdl.handle.net/2099/4089 |
| Access Level: | acceso abierto |
| Palabra clave: | Statistics Decision theory Maximum entropy Partial prior information Auditing Estadística Teoria de la decisió Classificació AMS::62 Statistics::62A01 Foundational and philosophical topics Classificació AMS::62 Statistics::62C Decision theory |
| Sumario: | There are two basic questions auditors and accountants must consider when developing test and estimation applications using Bayes' Theorem: What prior probability function should be used and what likelihood function should be used. In this paper we propose to use a maximum entropy prior probability function MEP with the most likely likelihood function MLL in the Quasi-Bayesian QB model introduced by McCray (1984). It is defined on an adequate parameter. Thus procedure only needs an expected value of θ0 known (in this paper the expected tainting) to obtain a MEP all an auditor or accountant need to supply are the range, as with any other prior, and the expected tainting, θ0. We will see some practical applications of the methodology proposed about internal control evaluation in auditing. |
|---|