Entropies of Mixing (EOM)and the Lorenz Order

Polynomial nonadditive, or pseudo-additive (PAE), entropies are related to the Shannon entropy in that both are derived from two classes of parent distributions of extreme-value theory, the Pareto and power distributions.The third class is the exponential distribution, corresponding to the Shannon entropy, to which the other two tend as their shape parameters increase without limit. These entropies all belong to a single class of entropies referred to as EOM. EOM is defined as the normalized difference between the dual of the Lorentz function and the Lorenz function. Sufficient conditions for majorization involve finding a separable, Schur-concave function, like the EOM, which increases as the distribution becomes more uniform or less spread out. Lorenz ordering has been associated to the degree in which the Lorenz curve is bent. This criterion is valid for tail distributions, and fails in the case where the distribution is limited on the right. EOM provide criteria for inequality in the Lorenz ordering sense: In the Pareto case,an increase in the shape parameter implies a decrease in inequality and the EOM decreases, whereas for the power distribution an increase in the shape parameter corresponds to an increase in inequality leading to an increase in the EOM. An analogy is drawn between Gauss' invariant distribution for the probability of the fractional part of a continued fraction and the area criterion in Lorenz ordering, analogous to the Gini index criterion. The tendency to approach the invariant distribution, as the number of partial quotients increases without limit, is shown to be analogous to the tendency to approach the invariant area, as the shape parameters increase without limit.

[1]  E. T. Jaynes,et al.  Papers on probability, statistics and statistical physics , 1983 .

[2]  Minaketan Behara,et al.  Additive and nonadditive measures of entropy , 1990 .

[3]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1967 .

[4]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[5]  C. Beck,et al.  Thermodynamics of chaotic systems: Spin systems , 1993 .

[6]  B. Arnold Majorization and the Lorenz Order: A Brief Introduction , 1987 .

[7]  A. W. Kemp,et al.  Kendall's Advanced Theory of Statistics. , 1994 .

[8]  R. Jackson Inequalities , 2007, Algebra for Parents.

[9]  M. Behara,et al.  Additive and non-additive entropies of finite measurable partitions , 1973 .

[10]  H. Cramér Mathematical methods of statistics , 1947 .

[11]  Joseph L. Gastwirth,et al.  A General Definition of the Lorenz Curve , 1971 .

[12]  H. Cramér Mathematical Methods of Statistics (PMS-9), Volume 9 , 1946 .

[13]  M. O. Lorenz,et al.  Methods of Measuring the Concentration of Wealth , 1905, Publications of the American Statistical Association.

[14]  C. Tsallis,et al.  Nonextensive Entropy: Interdisciplinary Applications , 2004 .

[15]  B. H. Lavenda Geometric Entropies of Mixing (EOM) , 2006, Open Syst. Inf. Dyn..

[16]  Karl Mosler,et al.  The Lorenz Zonoid of a Multivariate Distribution , 1996 .

[17]  C. Beck,et al.  Thermodynamics of chaotic systems : an introduction , 1993 .

[18]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[19]  I. Olkin,et al.  Inequalities: Theory of Majorization and Its Applications , 1980 .

[20]  Dick E. Boekee,et al.  The R-Norm Information Measure , 1980, Inf. Control..

[21]  H. Dalton The Measurement of the Inequality of Incomes , 1920 .

[22]  W. Feller,et al.  An Introduction to Probability Theory and Its Applications, Vol. II , 1972, The Mathematical Gazette.

[23]  L. Brillouin,et al.  Science and information theory , 1956 .

[24]  M. Kendall,et al.  Kendall's advanced theory of statistics , 1995 .