IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v48y2005i2p255-268.html
   My bibliography  Save this article

Extension to the product partition model: computing the probability of a change

Author

Listed:
  • Loschi, R.H.
  • Cruz, F.R.B.

Abstract

No abstract is available for this item.

Suggested Citation

  • Loschi, R.H. & Cruz, F.R.B., 2005. "Extension to the product partition model: computing the probability of a change," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 255-268, February.
  • Handle: RePEc:eee:csdana:v:48:y:2005:i:2:p:255-268
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(04)00049-0
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Fernando A. Quintana & Pilar L. Iglesias, 2003. "Bayesian clustering and product partition models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 557-574, May.
    2. Hawkins, Douglas M., 2001. "Fitting multiple change-point models to data," Computational Statistics & Data Analysis, Elsevier, vol. 37(3), pages 323-341, September.
    3. Loschi, R. H. & Cruz, F. R. B., 2002. "An analysis of the influence of some prior specifications in the identification of change points via product partition model," Computational Statistics & Data Analysis, Elsevier, vol. 39(4), pages 477-501, June.
    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. Chai, Jian & Lu, Quanying & Hu, Yi & Wang, Shouyang & Lai, Kin Keung & Liu, Hongtao, 2018. "Analysis and Bayes statistical probability inference of crude oil price change point," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 271-283.
    2. Cheon, Sooyoung & Kim, Jaehee, 2010. "Multiple change-point detection of multivariate mean vectors with the Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 406-415, February.
    3. Fuentes-García, Ruth & Mena, Ramsés H. & Walker, Stephen G., 2019. "Modal posterior clustering motivated by Hopfield’s network," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 92-100.
    4. An Cheng & Tonghui Chen & Guogang Jiang & Xinru Han, 2021. "Can Major Public Health Emergencies Affect Changes in International Oil Prices?," IJERPH, MDPI, vol. 18(24), pages 1-13, December.
    5. Hinoveanu, Laurentiu C. & Leisen, Fabrizio & Villa, Cristiano, 2019. "Bayesian loss-based approach to change point analysis," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 61-78.
    6. repec:jss:jstsof:23:i03 is not listed on IDEAS
    7. Ricardo C. Pedroso & Rosangela H. Loschi & Fernando Andrés Quintana, 2023. "Multipartition model for multiple change point identification," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 759-783, June.
    8. Erdman, Chandra & Emerson, John W., 2007. "bcp: An R Package for Performing a Bayesian Analysis of Change Point Problems," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i03).
    9. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
    10. Chai Jian & Wang Shubin & Xiao Hao, 2013. "Abrupt Changes of Global Oil Price," Journal of Systems Science and Information, De Gruyter, vol. 1(1), pages 38-59, February.

    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. Loschi, R.H. & Iglesias, P.L. & Arellano-Valle, R.B. & Cruz, F.R.B., 2007. "Full predictivistic modeling of stock market data: Application to change point problems," European Journal of Operational Research, Elsevier, vol. 180(1), pages 282-291, July.
    2. Chai, Jian & Lu, Quanying & Hu, Yi & Wang, Shouyang & Lai, Kin Keung & Liu, Hongtao, 2018. "Analysis and Bayes statistical probability inference of crude oil price change point," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 271-283.
    3. Chai Jian & Wang Shubin & Xiao Hao, 2013. "Abrupt Changes of Global Oil Price," Journal of Systems Science and Information, De Gruyter, vol. 1(1), pages 38-59, February.
    4. Rosangela Loschi & Leonardo Bastos & Pilar Iglesias, 2005. "Identifying Volatility Clusters Using the PPM: A Sensitivity Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 24(4), pages 305-319, June.
    5. Paul Fogel & Yann Gaston-Mathé & Douglas Hawkins & Fajwel Fogel & George Luta & S. Stanley Young, 2016. "Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health," IJERPH, MDPI, vol. 13(5), pages 1-14, May.
    6. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    7. Yann Guédon, 2013. "Exploring the latent segmentation space for the assessment of multiple change-point models," Computational Statistics, Springer, vol. 28(6), pages 2641-2678, December.
    8. Christopher A. Bush & Juhee Lee & Steven N. MacEachern, 2010. "Minimally informative prior distributions for non‐parametric Bayesian analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 253-268, March.
    9. Kang-Ping Lu & Shao-Tung Chang, 2021. "Robust Algorithms for Change-Point Regressions Using the t -Distribution," Mathematics, MDPI, vol. 9(19), pages 1-28, September.
    10. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    11. An Cheng & Tonghui Chen & Guogang Jiang & Xinru Han, 2021. "Can Major Public Health Emergencies Affect Changes in International Oil Prices?," IJERPH, MDPI, vol. 18(24), pages 1-13, December.
    12. Sylvia Frühwirth-Schnatter & Gertraud Malsiner-Walli, 2019. "From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering," 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. 13(1), pages 33-64, March.
    13. Galeano, Pedro, 2007. "The use of cumulative sums for detection of changepoints in the rate parameter of a Poisson Process," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6151-6165, August.
    14. Jaromír Antoch & Daniela Jarušková, 2013. "Testing for multiple change points," Computational Statistics, Springer, vol. 28(5), pages 2161-2183, October.
    15. Abel Rodr�guez & Enrique ter Horst, 2011. "Measuring expectations in options markets: an application to the S&P500 index," Quantitative Finance, Taylor & Francis Journals, vol. 11(9), pages 1393-1405, July.
    16. Sara Wade & Stephen G. Walker & Sonia Petrone, 2014. "A Predictive Study of Dirichlet Process Mixture Models for Curve Fitting," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 580-605, September.
    17. Ross, Gordon J., 2015. "Parametric and Nonparametric Sequential Change Detection in R: The cpm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i03).
    18. Batsidis, A. & Horváth, L. & Martín, N. & Pardo, L. & Zografos, K., 2013. "Change-point detection in multinomial data using phi-divergence test statistics," Journal of Multivariate Analysis, Elsevier, vol. 118(C), pages 53-66.
    19. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    20. Pedro Galeano & Dominik Wied, 2017. "Dating multiple change points in the correlation matrix," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 331-352, June.

    More about this item

    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:eee:csdana:v:48:y:2005:i:2:p:255-268. 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.elsevier.com/locate/csda .

    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.