An information-based tool for inferring the nature of deterministic sources in real data

Ribeiro, Ayrton; Riera, R.

doi:10.1016/j.physa.2013.06.053

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Information Theory [1] provides useful and unifying concepts that have been applied in many fields, including Physics, Engineering, Computer science, Statistics and Data Analysis, Linguistics, Marketing, Economics, and Complex Systems research in general [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Here, we are including Accounting as a potential field for interdisciplinary research where concepts and methods from information theory may have interesting applications.…”

Section: Introductionmentioning

confidence: 99%

Information theoretic approach for accounting classification

Ribeiro

Prataviera

2014

Physica A: Statistical Mechanics and its Applications

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In this paper we consider an information theoretic approach for the accounting classification process. We propose a matrix formalism and an algorithm for calculations of information theoretic measures associated to accounting classification. The formalism may be useful for further generalizations and computer-based implementation. Information theoretic measures, mutual information and symmetric uncertainty, were evaluated for daily transactions recorded in the chart of accounts of a small company during two years. Variation in the information measures due the aggregation of data in the process of accounting classification is observed. In particular, the symmetric uncertainty seems to be a useful parameter for comparing companies over time or in different sectors or different accounting choices and standards.

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Section: Introductionmentioning

confidence: 99%

Information theoretic approach for accounting classification

Ribeiro

Prataviera

2014

Physica A: Statistical Mechanics and its Applications

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“…In Fig. 5.7, we show the complexity measure C[P ] according to the Drift (µ/σ) and MR (k) parameter for several permutation order n. New structures were found at all scales, which means that new information is revealed as n increases (25).…”

Section: Assessing Permutation Entropy and Statistical Complexitymentioning

confidence: 99%