Information-Theoretic Distribution Test with Application to Normality

Thanasis Stengos and Ximing Wu† ()
Additional contact information
Ximing Wu†: Texas A&M University, USA and University of Guelph, Canada
Authors registered in the RePEc Author Service: Ximing Wu ()

Working Paper series from Rimini Centre for Economic Analysis

Abstract: We derive general distribution tests based on the method of Maximum Entropy density. The proposed tests are derived from maximizing the differential entropy subject to moment constraints. By exploiting the equivalence between the Maximum Entropy and Maximum Likelihood estimates of the general exponential family, we can use the conventional Likelihood Ratio, Wald and Lagrange Multiplier testing principles in the maximum entropy framework. In particular we use the Lagrange Multiplier method to derive tests for normality and their asymptotic properties. Monte Carlo evidence suggests that the proposed tests have desirable small sample properties.

Keywords: distribution test; maximum entropy; normality (search for similar items in EconPapers)
JEL-codes: C1 C12 C16 (search for similar items in EconPapers)
Date: 2007-07
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.rcea.org/RePEc/pdf/wp24_07.pdf

Related works:
Journal Article: Information-Theoretic Distribution Test with Application to Normality (2010)
Working Paper: Information-Theoretic Distribution Test with Application to Normality (2006)
Working Paper: Information-Theoretic Distribution Test with Application to Normality (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:24_07

Access Statistics for this paper

More papers in Working Paper series from Rimini Centre for Economic Analysis Contact information at EDIRC.
Bibliographic data for series maintained by Marco Savioli ().