IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v54y2002i3p543-564.html
   My bibliography  Save this article

Power Divergence Family of Tests for Categorical Time Series Models

Author

Listed:
  • Konstantinos Fokianos

Abstract

No abstract is available for this item.

Suggested Citation

  • Konstantinos Fokianos, 2002. "Power Divergence Family of Tests for Categorical Time Series Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(3), pages 543-564, September.
  • Handle: RePEc:spr:aistmt:v:54:y:2002:i:3:p:543-564
    DOI: 10.1023/A:1022459010316
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1022459010316
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1023/A:1022459010316?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. Fokianos, Konstantinos & Kedem, Benjamin, 1998. "Prediction and Classification of Non-stationary Categorical Time Series," Journal of Multivariate Analysis, Elsevier, vol. 67(2), pages 277-296, November.
    2. Ludwig Fahrmeir & Heinz Kaufmann, 1987. "Regression Models For Non‐Stationary Categorical Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 8(2), pages 147-160, 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. J. A. Pardo & M. C. Pardo, 2008. "Minimum Φ-Divergence Estimator and Φ-Divergence Statistics in Generalized Linear Models with Binary Data," Methodology and Computing in Applied Probability, Springer, vol. 10(3), pages 357-379, September.
    2. Munoz-Garcia, J. & Munoz-Pichardo, J.M. & Pardo, L., 2006. "Cressie and Read power-divergences as influence measures for logistic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3199-3221, July.

    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. Zhen, X. & Basawa, I.V., 2009. "Observation-driven generalized state space models for categorical time series," Statistics & Probability Letters, Elsevier, vol. 79(24), pages 2462-2468, December.
    2. Ginger M. Davis & Katherine B. Ensor, 2007. "Multivariate Time‐Series Analysis With Categorical and Continuous Variables in an Lstr Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(6), pages 867-885, November.
    3. Xu Gao & Daniel Gillen & Hernando Ombao, 2018. "Fisher information matrix of binary time series," METRON, Springer;Sapienza Università di Roma, vol. 76(3), pages 287-304, December.
    4. Heikki Kauppi, 2008. "Yield-Curve Based Probit Models for Forecasting U.S. Recessions: Stability and Dynamics," Discussion Papers 31, Aboa Centre for Economics.
    5. Pruscha Helmut & Göttlein Axel, 2003. "Forecasting of Categorical Time Series Using a Regression Model," Stochastics and Quality Control, De Gruyter, vol. 18(2), pages 223-240, January.
    6. Zhen, X. & Basawa, I.V., 2009. "Categorical time series models for contingency tables," Statistics & Probability Letters, Elsevier, vol. 79(10), pages 1331-1336, May.
    7. Yuichi Goto & Masanobu Taniguchi, 2020. "Discriminant analysis based on binary time series," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(5), pages 569-595, July.
    8. Sun-Joo Cho & Sarah Brown-Schmidt & Woo-yeol Lee, 2018. "Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 751-771, September.
    9. Brajendra C. Sutradhar, 2018. "Semi-parametric Dynamic Models for Longitudinal Ordinal Categorical Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 80-109, February.
    10. H. Kaufmann, 1988. "On existence and uniqueness of maximum likelihood estimates in quantal and ordinal response models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 35(1), pages 291-313, December.
    11. Xu Gao & Babak Shahbaba & Hernando Ombao, 2018. "Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 549-579, October.
    12. Moysiadis, Theodoros & Fokianos, Konstantinos, 2014. "On binary and categorical time series models with feedback," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 209-228.
    13. Moritz Berger & Gerhard Tutz, 2021. "Transition models for count data: a flexible alternative to fixed distribution models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1259-1283, October.
    14. Peiming Wang & Martin Puterman, 1999. "Markov Poisson regression models for discrete time series. Part 1: Methodology," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(7), pages 855-869.
    15. Song, Peter X.-K. & Freeland, R. Keith & Biswas, Atanu & Zhang, Shulin, 2013. "Statistical analysis of discrete-valued time series using categorical ARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 112-124.
    16. Brajendra C Sutradhar, 2018. "A Parameter Dimension-Split Based Asymptotic Regression Estimation Theory for a Multinomial Panel Data Model," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(2), pages 301-329, August.
    17. Chiranjit Dutta & Nalini Ravishanker & Sumanta Basu, 2022. "Modeling Multivariate Positive-Valued Time Series Using R-INLA," Papers 2206.05374, arXiv.org, revised Jul 2022.
    18. Konstantinos Fokianos & Benjamin Kedem, 2004. "Partial Likelihood Inference For Time Series Following Generalized Linear Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 173-197, March.
    19. Dag Tjøstheim, 2012. "Rejoinder on: Some recent theory for autoregressive count time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 469-476, September.
    20. Kurosawa T & Shimokawa A & Miyaoka E, 2017. "A Note on Transition Models for Binary 2×2 Cross-Over Data," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 3(5), pages 141-146, 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:aistmt:v:54:y:2002:i:3:p:543-564. 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.