IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v512y2018icp788-802.html
   My bibliography  Save this article

Information geometry on the curved q-exponential family with application to survival data analysis

Author

Listed:
  • Zhang, Fode
  • Ng, Hon Keung Tony
  • Shi, Yimin

Abstract

Information geometry and statistical manifold have attracted wide attention in the past few decades. The Amari–Chentsov structure plays a central role both in optimization and statistical inference because of the invariance under sufficient statistics. This paper discusses the Amari–Chentsov structure on statistical manifolds induced by the curved q-exponential family with Type-I censored data. The reliability function and cumulant generating function of the family are obtained, where the cumulant generating function contains a random variable depends on the experimental design. After the construction of the statistical manifold, the geometric quantities, i.e., Fisher metric, Amari tensor and connection coefficients are investigated. We find that the affine connection is not equal to 0, which is different from the existing results. The relationship among the geometric quantities based on the q-exponential family and curved q-exponential family is discussed. The relationship between the curvature on the manifold in the normal and tangent directions is obtained. Employing the maximum likelihood and Bayesian methods, the parameter vector and its dual are estimated. The main results are illustrated with the two-parameter q-exponential distribution and a real data analysis based on a terminal human cancer data set.

Suggested Citation

  • Zhang, Fode & Ng, Hon Keung Tony & Shi, Yimin, 2018. "Information geometry on the curved q-exponential family with application to survival data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 788-802.
    • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:788-802
    DOI: 10.1016/j.physa.2018.08.143
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118310835
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.08.143?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. Zhang, Fode & Ng, Hon Keung Tony & Shi, Yimin, 2018. "On alternative q-Weibull and q-extreme value distributions: Properties and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1171-1190.
    2. Saberian, E., 2018. "On the spectrum of plasma modes in a field-free pair plasma: Dispersion and Landau damping in Tsallis statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 289-299.
    3. Cafaro, Carlo, 2017. "Geometric algebra and information geometry for quantum computational software," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 154-196.
    4. Kumar, Vikas, 2016. "Some results on Tsallis entropy measure and k-record values," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 667-673.
    5. Amari, Shun-ichi & Ohara, Atsumi & Matsuzoe, Hiroshi, 2012. "Geometry of deformed exponential families: Invariant, dually-flat and conformal geometries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4308-4319.
    6. Zhang, Fode & Shi, Yimin & Wang, Ruibing, 2017. "Geometry of the q-exponential distribution with dependent competing risks and accelerated life testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 552-565.
    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. Agahi, Hamzeh & Alipour, Mohsen, 2020. "Tsallis–Mittag-Leffler distribution and its applications in gas prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    2. Hideto Nakashima & Piotr Graczyk, 2022. "Wigner and Wishart ensembles for sparse Vinberg models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 399-433, June.

    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. Fode Zhang & Hon Keung Tony Ng & Yimin Shi & Ruibing Wang, 2019. "Amari–Chentsov structure on the statistical manifold of models for accelerated life tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 77-105, March.
    2. Zhang, Fode & Ng, Hon Keung Tony & Shi, Yimin, 2018. "On alternative q-Weibull and q-extreme value distributions: Properties and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1171-1190.
    3. Wang, Liang & Tripathi, Yogesh Mani & Dey, Sanku & Zhang, Chunfang & Wu, Ke, 2022. "Analysis of dependent left-truncated and right-censored competing risks data with partially observed failure causes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 194(C), pages 285-307.
    4. K. V. Harsha & Alladi Subramanyam, 2020. "Some information inequalities for statistical inference," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(5), pages 1237-1256, October.
    5. Negreiros, Ana Cláudia Souza Vidal de & Lins, Isis Didier & Moura, Márcio José das Chagas & Droguett, Enrique López, 2020. "Reliability data analysis of systems in the wear-out phase using a (corrected) q-Exponential likelihood," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    6. Olawale Ayoade & Pablo Rivas & Javier Orduz, 2022. "Artificial Intelligence Computing at the Quantum Level," Data, MDPI, vol. 7(3), pages 1-16, February.
    7. Pessoa, Pedro & Cafaro, Carlo, 2021. "Information geometry for Fermi–Dirac and Bose–Einstein quantum statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    8. Zhang, Fode & Ng, Hon Keung Tony & Shi, Yimin, 2020. "Mis-specification analysis of Wiener degradation models by using f-divergence with outliers," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    9. Agahi, Hamzeh & Alipour, Mohsen, 2020. "Tsallis–Mittag-Leffler distribution and its applications in gas prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    10. Aswathy S. Krishnan & S. M. Sunoj & P. G. Sankaran, 2019. "Quantile-based reliability aspects of cumulative Tsallis entropy in past lifetime," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(1), pages 17-38, January.
    11. Jafar Ahmadi, 2021. "Characterization of continuous symmetric distributions using information measures of records," Statistical Papers, Springer, vol. 62(6), pages 2603-2626, December.
    12. Kumar, Vikas & Rekha,, 2018. "A quantile approach of Tsallis entropy for order statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 916-928.
    13. K.V., Harsha & K.S., Subrahamanian Moosath, 2015. "Dually flat geometries of the deformed exponential family," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 136-147.
    14. Zhang, Fode & Shi, Yimin, 2016. "Geometry of exponential family with competing risks and censored data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 234-245.
    15. Nelson, Kenric P., 2022. "Independent Approximates enable closed-form estimation of heavy-tailed distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 601(C).
    16. Balakrishnan, Narayanaswamy & Buono, Francesco & Longobardi, Maria, 2022. "A unified formulation of entropy and its application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    17. Calì, Camilla & Longobardi, Maria & Ahmadi, Jafar, 2017. "Some properties of cumulative Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1012-1021.

    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:eee:phsmap:v:512:y:2018:i:c:p:788-802. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.