Skip to main content
Log in

Pseudo-exponential distribution and its statistical applications in econophysics

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In generalized measure theory, \(\sigma \)-\(\oplus \)-measure is a generalization of the classical measure defined on a pseudo-addition. In this paper, the class of pseudo-exponential distributions based on a class of \(\sigma \)-\(\oplus \)-measure is introduced. Some examples of this class are investigated. Then by two real data sets obtained from the last three decades of oil, and the last two decades of the daily natural gas spot prices, we show that the pseudo-exponential distribution is better fitted than exponential distribution using the AIC and BIC information criteria.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Banerjee A, Yakovenko VM, Di Matteo T (2006) A study of the personal income distribution in Australia. Phys A Stat Mech Appl 370:54–59

    Article  MathSciNet  Google Scholar 

  • Bede B, O’ Regan D (2013) The theory of pseudo-linear operators. Knowl Based Syst 38:19–26

    Article  Google Scholar 

  • Bernasconi J (1979) Anomalous frequency-dependent conductivity in disordered one-dimensional systems. Phys Rev Lett 42:819–822

    Article  Google Scholar 

  • Budiyono A (2013) Quantization from an exponential distribution of infinitesimal action. Phys A Stat Mech Appl 392:307–313

    Article  MathSciNet  Google Scholar 

  • Collier MR (2004) Are magnetospheric suprathermal particle distributions (\(\kappa \) functions) inconsistent with maximum entropy considerations? Adv Space Res 33:2108–2112

    Article  Google Scholar 

  • Drăgulescu A, Yakovenko VM (2001) Exponential and power-law probability distributions of wealth and income in the United Kingdom and the United States. Phys A Stat Mech Appl 299:213–221

    Article  MATH  Google Scholar 

  • Field PR, Hogan RJ, Brown PRA, Illingworth AJ, Choularton TW, Cotton RJ (2005) Parametrization of ice particle size distributions for midlatitude stratiform cloud. Q J R Meteorol Soc 131:1997–2017

    Article  Google Scholar 

  • Granato AV, Lücke K (1956) Theory of mechanical damping due to dislocations. J Appl Phys 27:583–593

    Article  MATH  Google Scholar 

  • Kakalios J, Street RA, Jackson WB (1987) Stretched-exponential relaxation arising from dispersive diffusion of hydrogen in amorphous silicon. Phys Rev Lett 59:1037–1040

    Article  Google Scholar 

  • Kolokoltsov VN, Maslov VP (1997) Idempotent analysis and its applications. Kluwer Academic Publishers, Dordrecht

    Book  MATH  Google Scholar 

  • Kuich W (1986) Semirings, automata, languages. Springer, Berlin

    Book  MATH  Google Scholar 

  • Litvinov GL, Maslov VP (1996) Idempotent mathematics: correspondence principle and applications. Russ Math Surv 51:1210–1211

    Article  MATH  Google Scholar 

  • Macdonald JR (1985) Frequency response of unified dielectric and conductive systems involving an exponential distribution of activation energies. J Appl Phys 58(5):1955–1970

    Article  MathSciNet  Google Scholar 

  • Mantegna RN, Stanley HE (1999) Introduction to econophysics: correlations and complexity in finance. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Mao Y-Y, Strigari LE, Wechsler RH, Wu H-Y, Hahn O (2013) Halo-to-halo similarity and scatter in the velocity distribution of dark matter. Astrophys J 764:35

    Article  Google Scholar 

  • Mesiar R, Pap E (1999) Idempotent integral as limit of \(g\) -integrals. Fuzzy Sets Syst 102:385–392

    Article  MathSciNet  MATH  Google Scholar 

  • Pap E (1993) \(g\)-calculus. Univ u Novom Sadu Zb Rad Prirod-Mat Fak Ser Mat 23:145–156

    MATH  Google Scholar 

  • Pap E (1995) Null-additive set functions. Kluwer Academic Publishers, Dordrecht

    MATH  Google Scholar 

  • Pap E (2002) Pseudo-additive measures and their applications. In: Pap E (ed) Handbook of measure theory, vol 2. Elsevier, Amsterdam, pp 1403–1465

    Chapter  Google Scholar 

  • Pap E (1997) Pseudo-analysis as a mathematical base for soft computing. Soft Comput 1:61–68

    Article  Google Scholar 

  • Pap E (2005) Application of the generated pseudo-analysis on nonlinear partial differentialequations. In: Litvinov GL, Maslov VP (eds) Proceedings of the conference on idempotent mathematics and mathematical physics, contemporary mathematics, vol 377. American Mathematical Society, pp 239-259

  • Pap E (2008) Pseudo-analysis approach to nonlinear partial differential equations. Acta Polytechnica Hungarica 5:31–45

    Google Scholar 

  • Pap E, Ralević N (1998) Pseudo-Laplace transform. Nonlinear Anal 33:553–560

    Article  MathSciNet  MATH  Google Scholar 

  • Pap E, Štajner I (1999) Generalized pseudo-convolution in the theory of probabilistic metric spaces, information, fuzzy numbers, optimization, system theory. Fuzzy Sets Syst 102:393–415

    Article  MathSciNet  MATH  Google Scholar 

  • Pap E, Štrboja M, Rudas I (2014) Pseudo-\(L^{p}\) space and convergence. Fuzzy Sets Syst 238:113–128

    Article  MATH  Google Scholar 

  • Schwartz EL (1993) Computational neuroscience. MIT Press, Cambridge

    Google Scholar 

  • Trappenberg T (2009) Fundamentals of computational neuroscience. OUP Oxford, Oxford

    MATH  Google Scholar 

  • Wannier GH (2010) Statistical physics. Dover, New York

    MATH  Google Scholar 

  • Zeman A, Brooks KR, Ghebreab S (2015) An exponential filter model predicts lightness illusions. Front Hum Neurosci 9:PMC4478851

    Article  Google Scholar 

Download references

Acknowledgements

The authors are very grateful to the anonymous reviewers for their suggestions that have led to a revised version of this paper. Hossein Mehri-Dehnavi was supported by Babol Noshirvani University of Technology with Grant program No. BNUT/390012/97. Hamzeh Agahi was supported by Babol Noshirvani University of Technology with Grant program No. BNUT/392100/97.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hossein Mehri-Dehnavi.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mehri-Dehnavi, H., Agahi, H. & Mesiar, R. Pseudo-exponential distribution and its statistical applications in econophysics. Soft Comput 23, 357–363 (2019). https://doi.org/10.1007/s00500-018-3623-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3623-x

Keywords

Navigation