IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v18y2016i3d10.1007_s11009-016-9495-6.html
   My bibliography  Save this article

Dependence Assessment Based on Generalized Relative Complexity: Application to Sampling Network Design

Author

Listed:
  • F. J. Alonso

    (University of Granada)

  • M. C. Bueso

    (Technical University of Cartagena)

  • J. M. Angulo

    (University of Granada)

Abstract

Generalized statistical complexity measures provide a means to jointly quantify inner information and relative structural richness of a system described in terms of a probability model. As a natural divergence-based extension in this context, generalized relative complexity measures have been proposed for the local comparison of two given probability distributions. In this paper, the behavior of generalized relative complexity measures is studied for assessment of structural dependence in a random vector leading to a concept of ‘generalized mutual complexity’. A related optimality criterion for sampling network design, which provides an alternative to mutual information based methods in the complexity context, is formulated. Aspects related to practical implementation, and conceptual issues regarding the meaning and potential use of this approach, are discussed. Numerical examples are used for illustration.

Suggested Citation

  • F. J. Alonso & M. C. Bueso & J. M. Angulo, 2016. "Dependence Assessment Based on Generalized Relative Complexity: Application to Sampling Network Design," Methodology and Computing in Applied Probability, Springer, vol. 18(3), pages 921-933, September.
    • Handle: RePEc:spr:metcap:v:18:y:2016:i:3:d:10.1007_s11009-016-9495-6
    DOI: 10.1007/s11009-016-9495-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11009-016-9495-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11009-016-9495-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Caselton, W. F. & Zidek, J. V., 1984. "Optimal monitoring network designs," Statistics & Probability Letters, Elsevier, vol. 2(4), pages 223-227, August.
    2. Martin, M.T. & Plastino, A. & Rosso, O.A., 2006. "Generalized statistical complexity measures: Geometrical and analytical properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 439-462.
    3. Zdravko I. Botev & Dirk P. Kroese, 2011. "The Generalized Cross Entropy Method, with Applications to Probability Density Estimation," Methodology and Computing in Applied Probability, Springer, vol. 13(1), pages 1-27, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Angulo, J. & Angulo, J.C. & Angulo, J.M., 2018. "An application of information theory to stochastic classical gravitational fields," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 129-141.

    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. Aquino, Andre L.L. & Ramos, Heitor S. & Frery, Alejandro C. & Viana, Leonardo P. & Cavalcante, Tamer S.G. & Rosso, Osvaldo A., 2017. "Characterization of electric load with Information Theory quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 277-284.
    2. Bariviera, Aurelio F. & Font-Ferrer, Alejandro & Sorrosal-Forradellas, M. Teresa & Rosso, Osvaldo A., 2019. "An information theory perspective on the informational efficiency of gold price," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    3. Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Tabak, Benjamin M., 2021. "Insights from the (in)efficiency of Chinese sectoral indices during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    4. Montani, Fernando & Deleglise, Emilia B. & Rosso, Osvaldo A., 2014. "Efficiency characterization of a large neuronal network: A causal information approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 58-70.
    5. Calbet, Xavier & López-Ruiz, Ricardo, 2007. "Extremum complexity distribution of a monodimensional ideal gas out of equilibrium," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 523-530.
    6. Yildiz, Anil & Mern, John & Kochenderfer, Mykel J. & Howland, Michael F., 2023. "Towards sequential sensor placements on a wind farm to maximize lifetime energy and profit," Renewable Energy, Elsevier, vol. 216(C).
    7. Aurelio Fernandez Bariviera & María Belén Guercio & Lisana B. Martinez & Osvaldo A. Rosso, 2015. "The (in)visible hand in the Libor market: an information theory approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(8), pages 1-9, August.
    8. Francisco Javier Alonso & María del Carmen Bueso & José Miguel Angulo, 2012. "Effect of Data Transformations on Predictive Risk Indicators," Methodology and Computing in Applied Probability, Springer, vol. 14(3), pages 705-716, September.
    9. Zhang, Boyi & Shang, Pengjian & Zhou, Qin, 2021. "The identification of fractional order systems by multiscale multivariate analysis," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    10. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
    11. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2019. "Exploring disorder and complexity in the cryptocurrency space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 548-556.
    12. Redelico, Francisco O. & Traversaro, Francisco & Oyarzabal, Nicolás & Vilaboa, Ivan & Rosso, Osvaldo A., 2017. "Evaluation of the status of rotary machines by time causal Information Theory quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 321-329.
    13. Guarino, Francesco & Cassarà, Pietro & Longo, Sonia & Cellura, Maurizio & Ferro, Erina, 2015. "Load match optimisation of a residential building case study: A cross-entropy based electricity storage sizing algorithm," Applied Energy, Elsevier, vol. 154(C), pages 380-391.
    14. Amadio, Ariel & Rey, Andrea & Legnani, Walter & Blesa, Manuel García & Bonini, Cristian & Otero, Dino, 2023. "Mathematical and informational tools for classifying blood glucose signals - a pilot study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    15. Mateos, Diego M. & Gómez-Ramírez, Jaime & Rosso, Osvaldo A., 2021. "Using time causal quantifiers to characterize sleep stages," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    16. Lima, David H.S. & Aquino, Andre L.L. & Rosso, Osvaldo A. & Curado, Marilia, 2024. "Characterization of task allocation techniques in data centers based on information theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    17. de Novaes Pires Leite, Gustavo & da Cunha, Guilherme Tenório Maciel & dos Santos Junior, José Guilhermino & Araújo, Alex Maurício & Rosas, Pedro André Carvalho & Stosic, Tatijana & Stosic, Borko & Ros, 2021. "Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines," Renewable Energy, Elsevier, vol. 164(C), pages 1183-1194.
    18. Argyroudis, George S. & Siokis, Fotios M., 2019. "Spillover effects of Great Recession on Hong-Kong’s Real Estate Market: An analysis based on Causality Plane and Tsallis Curves of Complexity–Entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 576-586.
    19. Jauregui, M. & Zunino, L. & Lenzi, E.K. & Mendes, R.S. & Ribeiro, H.V., 2018. "Characterization of time series via Rényi complexity–entropy curves," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 74-85.
    20. Bueso, M. C. & Angulo, J. M. & Qian, G. & Alonso, F. J., 1999. "Spatial Sampling Design Based on Stochastic Complexity," Journal of Multivariate Analysis, Elsevier, vol. 71(1), pages 94-110, October.

    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:spr:metcap:v:18:y:2016:i:3:d:10.1007_s11009-016-9495-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.