IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v42y2003i1-2p91-109.html
   My bibliography  Save this article

Bayesian and profile likelihood change point methods for modeling cognitive function over time

Author

Listed:
  • Hall, Charles B.
  • Ying, Jun
  • Kuo, Lynn
  • Lipton, Richard B.

Abstract

No abstract is available for this item.

Suggested Citation

  • Hall, Charles B. & Ying, Jun & Kuo, Lynn & Lipton, Richard B., 2003. "Bayesian and profile likelihood change point methods for modeling cognitive function over time," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 91-109, February.
    • Handle: RePEc:eee:csdana:v:42:y:2003:i:1-2:p:91-109
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(02)00148-2
    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. Bradley P. Carlin & Alan E. Gelfand & Adrian F. M. Smith, 1992. "Hierarchical Bayesian Analysis of Changepoint Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 389-405, 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. Alexander C. McLain & Paul S. Albert, 2014. "Modeling longitudinal data with a random change point and no time-zero: Applications to inference and prediction of the labor curve," Biometrics, The International Biometric Society, vol. 70(4), pages 1052-1060, December.
    2. Getachew A. Dagne, 2021. "Bayesian Quantile Bent-Cable Growth Models for Longitudinal Data with Skewness and Detection Limit," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 129-141, April.
    3. Chenxi Li & N. Maritza Dowling & Rick Chappell, 2015. "Quantile regression with a change‐point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease," Biometrics, The International Biometric Society, vol. 71(3), pages 625-635, September.
    4. Perez, C.J. & Martin, J. & Rufo, M.J., 2006. "MCMC-based local parametric sensitivity estimations," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 823-835, November.
    5. G. Muniz Terrera & A. van den Hout & F. E. Matthews, 2011. "Random change point models: investigating cognitive decline in the presence of missing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(4), pages 705-716, November.
    6. van den Hout, Ardo & Muniz-Terrera, Graciela & Matthews, Fiona E., 2013. "Change point models for cognitive tests using semi-parametric maximum likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 684-698.
    7. Gebrenegus Ghilagaber & Parfait Munezero, 2020. "Bayesian change-point modelling of the effects of 3-points-for-a-win rule in football," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(2), pages 248-264, January.
    8. Pérez, C.J. & Martín, J. & Rufo, M.J., 2006. "Sensitivity estimations for Bayesian inference models solved by MCMC methods," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1310-1314.
    9. Zhao, L. & Banerjee, M., 2012. "Bayesian piecewise mixture model for racial disparity in prostate cancer progression," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 362-369.
    10. El-Bassiouni, M. Y. & Charif, H. A., 2004. "Testing a null variance ratio in mixed models with zero degrees of freedom for error," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 707-719, July.
    11. Hélène Jacqmin-Gadda & Daniel Commenges & Jean-François Dartigues, 2006. "Random Changepoint Model for Joint Modeling of Cognitive Decline and Dementia," Biometrics, The International Biometric Society, vol. 62(1), pages 254-260, March.

    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. DAVID E. ALLEN & MICHAEL McALEER & ROBERT J. POWELL & ABHAY K. SINGH, 2018. "Non-Parametric Multiple Change Point Analysis Of The Global Financial Crisis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-23, June.
    2. Fitzpatrick, Matthew, 2014. "Geometric ergodicity of the Gibbs sampler for the Poisson change-point model," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 55-61.
    3. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 553-583.
    4. Owyang, Michael T. & Piger, Jeremy & Wall, Howard J., 2008. "A state-level analysis of the Great Moderation," Regional Science and Urban Economics, Elsevier, vol. 38(6), pages 578-589, November.
    5. Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.
    6. Michael W. Robbins & Colin M. Gallagher & Robert B. Lund, 2016. "A General Regression Changepoint Test for Time Series Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 670-683, April.
    7. Cathy W. S. Chen & Mike K. P. So, 2003. "Subset threshold autoregression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 49-66.
    8. Fernando Ferraz do Nascimento & Wyara Vanesa Moura e Silva, 2017. "A Bayesian model for multiple change point to extremes, with application to environmental and financial data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2410-2426, October.
    9. Ľluboš Pástor & Robert F. Stambaugh, 2001. "The Equity Premium and Structural Breaks," Journal of Finance, American Finance Association, vol. 56(4), pages 1207-1239, August.
    10. Gordon, Stephen & Bélanger, Gilles, 1996. "Échantillonnage de Gibbs et autres applications économétriques des chaînes markoviennes," L'Actualité Economique, Société Canadienne de Science Economique, vol. 72(1), pages 27-49, mars.
    11. Gary M. Koop & Simon M. Potter, 2004. "Forecasting and Estimating Multiple Change-point Models with an Unknown Number of Change-points," Discussion Papers in Economics 04/31, Division of Economics, School of Business, University of Leicester.
    12. Rotondi, R., 2002. "On the influence of the proposal distributions on a reversible jump MCMC algorithm applied to the detection of multiple change-points," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 633-653, September.
    13. Ardia, David & Dufays, Arnaud & Ordás Criado, Carlos, 2023. "Linking Frequentist and Bayesian Change-Point Methods," MPRA Paper 119486, University Library of Munich, Germany.
    14. Tian, Guo-Liang & Ng, Kai Wang & Tan, Ming, 2008. "EM-type algorithms for computing restricted MLEs in multivariate normal distributions and multivariate t-distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4768-4778, June.
    15. Li Zhaoyuan & Tian Maozai, 2017. "Detecting Change-Point via Saddlepoint Approximations," Journal of Systems Science and Information, De Gruyter, vol. 5(1), pages 48-73, February.
    16. Rosalia Condorelli, 2013. "A Bayesian analysis of suicide data," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(2), pages 1143-1161, February.
    17. Eric F. Lock & Nidhi Kohli & Maitreyee Bose, 2018. "Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 733-750, September.
    18. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(4), pages 1057-1084.
    19. Tian, Guo-Liang & Ng, Kai Wang & Li, Kai-Can & Tan, Ming, 2009. "Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of Haemolytic uraemic syndrome," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3314-3323, July.
    20. R. Rotondi & E. Garavaglia, 2002. "Statistical Analysis of the Completeness of a Seismic Catalogue," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 25(3), pages 245-258, March.

    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:42:y:2003:i:1-2:p:91-109. 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.