IDEAS home Printed from https://ideas.repec.org/a/anm/alpnmr/v3y2015i1p7-14.html
   My bibliography  Save this article

Performance Of Shannon's Maximum Entropy Distribution Under Some Restrictions: An Application On Turkey's Annual Temperatures

Author

Listed:
  • Hatice Çiçek
  • Sinan Saraçlı

Abstract

Entropy has a very important role in Statistics. In recent studies it can be seen that entropy started to take place nearly in every brunch of science. In information theory, entropy is a measure of the uncertainty in a random variable. While there are different kinds of methods in entropy, the most common maximum entropy (MaxEnt) method maximizes the Shannon’s entropy according to the restrictions which are obtained from the random variables. MaxEnt distribution is the distribution which is obtained by this method. The purpose of this study is to calculate the MaxEnt distribution of Turkey’s Annual temperatures for last 43 years under combinations of the restrictions 1, x, x2, lnx, (lnx)2, ln(1+x2) and to compare this distribution with the real probability distribution by the help of Kolmogorov-Smirnov goodness of fit test. According to the results, goodness of fit statistics accept the null hypothesis that all the entropy distributions fit with the probability distribution. The results are given in related tables and figures.

Suggested Citation

  • Hatice Çiçek & Sinan Saraçlı, 2015. "Performance Of Shannon's Maximum Entropy Distribution Under Some Restrictions: An Application On Turkey's Annual Temperatures," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 3(1), pages 7-14, June.
  • Handle: RePEc:anm:alpnmr:v:3:y:2015:i:1:p:7-14
    DOI: http://dx.doi.org/10.17093/aj.2015.3.1.5000128274
    as

    Download full text from publisher

    File URL: https://www.alphanumericjournal.com/media/Issue/volume-3-issue-1-2015/performance-of-shannons-maximum-entropy-distribution-under-s_72vPeiY.pdf
    Download Restriction: no

    File URL: https://alphanumericjournal.com/article/performance-of-shannons-maximum-entropy-distribution-under-some-restrictions-an-application-on-turkeys-annual-temperatures/
    Download Restriction: no

    File URL: https://libkey.io/http://dx.doi.org/10.17093/aj.2015.3.1.5000128274?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wu, Ximing & Perloff, Jeffrey M., 2007. "GMM estimation of a maximum entropy distribution with interval data," Journal of Econometrics, Elsevier, vol. 138(2), pages 532-546, June.
    2. Wu, Ximing, 2003. "Calculation of maximum entropy densities with application to income distribution," Journal of Econometrics, Elsevier, vol. 115(2), pages 347-354, August.
    3. Brockett, Patrick L. & Charnes, Abraham & Cooper, William W. & Learner, David & Phillips, Fred Y., 1995. "Information theory as a unifying statistical approach for use in marketing research," European Journal of Operational Research, Elsevier, vol. 84(2), pages 310-329, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yiguo Sun & Thanasis Stengos, 2008. "The absolute health income hypothesis revisited: a semiparametric quantile regression approach," Empirical Economics, Springer, vol. 35(2), pages 395-412, September.
    2. Sung Y. Park & Anil K. Bera, 2018. "Information theoretic approaches to income density estimation with an application to the U.S. income data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(4), pages 461-486, December.
    3. Lee, Jongchul, 2013. "A provincial perspective on income inequality in urban China and the role of property and business income," China Economic Review, Elsevier, vol. 26(C), pages 140-150.
    4. Ximing Wu & Thanasis Stengos, 2005. "Partially adaptive estimation via the maximum entropy densities," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 352-366, December.
    5. Ba Chu & Stephen Satchell, 2016. "Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence," Econometrics, MDPI, vol. 4(2), pages 1-21, March.
    6. Tack, Jesse, 2013. "A Nested Test for Common Yield Distributions with Applications to U.S. Corn," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(1), pages 1-14, April.
    7. Jesse B. Tack & David Ubilava, 2015. "Climate and agricultural risk: measuring the effect of ENSO on U.S. crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 46(2), pages 245-257, March.
    8. Carol Alexander & José María Sarabia, 2012. "Quantile Uncertainty and Value‐at‐Risk Model Risk," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1293-1308, August.
    9. Tanaka, Ken'ichiro & Toda, Alexis Akira, 2015. "Discretizing Distributions with Exact Moments: Error Estimate and Convergence Analysis," University of California at San Diego, Economics Working Paper Series qt7g23r5kh, Department of Economics, UC San Diego.
    10. Beatty, Timothy K.M. & LaFrance, Jeffrey T., 2005. "U.S. Demand for Food and Nutrition in the 20th Century," CUDARE Working Papers 25105, University of California, Berkeley, Department of Agricultural and Resource Economics.
    11. Wu, Ximing & Perloff, Jeffrey M., 2004. "China's Income Distribution Over Time: Reasons for Rising Inequality," Institute for Research on Labor and Employment, Working Paper Series qt9jw2v939, Institute of Industrial Relations, UC Berkeley.
    12. Matthew Wiser & Gloria Yeomans-Maldonado & Sudipta Sarangi, 2014. "Having More Fun with Organized Kissing," Southern Economic Journal, John Wiley & Sons, vol. 80(3), pages 855-865, January.
    13. Wu, Ximing & Perloff, Jeffrey M., 2007. "Information-Theoretic Deconvolution Approximation of Treatment Effect Distribution," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt6bm6n30x, Department of Agricultural & Resource Economics, UC Berkeley.
    14. Camelia Minoiu & Sanjay Reddy, 2014. "Kernel density estimation on grouped data: the case of poverty assessment," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(2), pages 163-189, June.
    15. Asok K. Nanda & Shovan Chowdhury, 2021. "Shannon's Entropy and Its Generalisations Towards Statistical Inference in Last Seven Decades," International Statistical Review, International Statistical Institute, vol. 89(1), pages 167-185, April.
    16. Gholamreza Hajargsht & William E. Griffiths & Joseph Brice & D.S. Prasada Rao & Duangkamon Chotikapanich, 2011. "GMM Estimation of Income Distributions from Grouped Data," Department of Economics - Working Papers Series 1129, The University of Melbourne.
    17. Glover, Fred & Sueyoshi, Toshiyuki, 2009. "Contributions of Professor William W. Cooper in Operations Research and Management Science," European Journal of Operational Research, Elsevier, vol. 197(1), pages 1-16, August.
    18. Jenny Farmer & Donald Jacobs, 2018. "High throughput nonparametric probability density estimation," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-29, May.
    19. Domenico Di Gangi & Fabrizio Lillo & Davide Pirino, 2015. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Papers 1509.00607, arXiv.org, revised Jul 2018.
    20. Yichen Gao & Yu Zhang & Ximing Wu, 2015. "Penalized exponential series estimation of copula densities with an application to intergenerational dependence of body mass index," Empirical Economics, Springer, vol. 48(1), pages 61-81, February.

    More about this item

    Keywords

    Discrete Distributions; Lagrange Multipliers; Shannon’s Maximum Entropy Distribution;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:anm:alpnmr:v:3:y:2015:i:1:p:7-14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bahadir Fatih Yildirim (email available below). General contact details of provider: https://www.alphanumericjournal.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.