IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v29y2012i3p341-362.html
   My bibliography  Save this article

Mixtures of Autoregressions with an Improper Component for Panel Data

Author

Listed:
  • Nicholas Longford
  • Pierpaolo D’Urso

Abstract

An EM algorithm for fitting mixtures of autoregressions of low order is constructed and the properties of the estimators are explored on simulated and real datasets. The mixture model incorporates a component with an improper density, which is intended for outliers. The model is proposed as an alternative to the search for the order of a single-component autoregression. The methods can be adapted to other patterns of dependence in panel data. An application to the monthly records of income of the outlets of a retail company is presented. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Nicholas Longford & Pierpaolo D’Urso, 2012. "Mixtures of Autoregressions with an Improper Component for Panel Data," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 341-362, October.
    • Handle: RePEc:spr:jclass:v:29:y:2012:i:3:p:341-362
    DOI: 10.1007/s00357-012-9111-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00357-012-9111-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00357-012-9111-6?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. Pietro Coretto & Christian Hennig, 2010. "A simulation study to compare robust clustering methods based on mixtures," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(2), pages 111-135, September.
    2. Longford, N.T. & Pittau, M.G., 2006. "Stability of household income in European countries in the 1990s," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1364-1383, November.
    3. N. T. Longford & Pierpaolo D'Urso, 2011. "Mixture models with an improper component," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2511-2521, January.
    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. Jitka Bartošová & Nicholas T. Longford, 2014. "A Study of Income Stability in the Czech Republic by Finite Mixtures," Prague Economic Papers, Prague University of Economics and Business, vol. 2014(3), pages 330-348.
    2. Utkarsh J. Dang & Ryan P. Browne & Paul D. McNicholas, 2015. "Mixtures of multivariate power exponential distributions," Biometrics, The International Biometric Society, vol. 71(4), pages 1081-1089, December.
    3. L. García-Escudero & A. Gordaliza & A. Mayo-Iscar, 2014. "A constrained robust proposal for mixture modeling avoiding spurious solutions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 27-43, March.
    4. Longford, N.T. & Pittau, M.G., 2006. "Stability of household income in European countries in the 1990s," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1364-1383, November.
    5. Luca De Angelis, 2013. "Latent class models for financial data analysis: some statistical developments," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 227-242, June.
    6. Christian Hennig, 2013. "Discussion of “Model-based clustering with non-normal mixture distributions” by S. X. Lee and G. J. McLachlan," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 455-458, November.
    7. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.
    8. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
    9. Pietro Coretto, 2022. "Estimation and computations for Gaussian mixtures with uniform noise under separation constraints," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 427-458, June.
    10. Huiwen Wang & Shan Lu & Jichang Zhao, 2018. "Aggregating multiple types of complex data in stock market prediction: A model-independent framework," Papers 1805.05617, arXiv.org.
    11. Borodkin, Fridrich & Bragin, Vladimir & Shpack, Maria, 2006. "Stability of Incomes Distribution in Modern Russia (1994–2004)," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 3(3), pages 17-67.
    12. Jitka Bartošová & Vladislav Bína, 2009. "Modelling of Income Distribution of Czech Households in The Years 1996-2005," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2009(4), pages 3-18.
    13. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    14. Nicholas T. Longford, 2013. "Searching for contaminants," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 2041-2055, September.
    15. Andrea Cerioli & Domenico Perrotta, 2014. "Robust clustering around regression lines with high density regions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(1), pages 5-26, March.
    16. Nicholas Longford, 2008. "Inference with the lognormal distribution," Economics Working Papers 1104, Department of Economics and Business, Universitat Pompeu Fabra.
    17. Yongku Kim & Woo Dong Lee & Sang Gil Kang, 2020. "A matching prior based on the modified profile likelihood for the common mean in multiple log-normal distributions," Statistical Papers, Springer, vol. 61(2), pages 543-573, April.

    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:jclass:v:29:y:2012:i:3:p:341-362. 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.