IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v59y2003i3p650-660.html
   My bibliography  Save this article

Bayesian Deconvolution Analysis of Pulsatile Hormone Concentration Profiles

Author

Listed:
  • Timothy D. Johnson

Abstract

No abstract is available for this item.

Suggested Citation

  • Timothy D. Johnson, 2003. "Bayesian Deconvolution Analysis of Pulsatile Hormone Concentration Profiles," Biometrics, The International Biometric Society, vol. 59(3), pages 650-660, September.
    • Handle: RePEc:bla:biomet:v:59:y:2003:i:3:p:650-660
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/1541-0420.00075
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    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. Andrés Christen & Bruno Sansó & Mario Santana-Cibrian & Jorge X. Velasco-Hernández, 2016. "Bayesian deconvolution of oil well test data using Gaussian processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 721-737, March.
    2. Huayu Liu & Nichole E. Carlson & Gary K. Grunwald & Alex J. Polotsky, 2018. "Modeling associations between latent event processes governing time series of pulsing hormones," Biometrics, The International Biometric Society, vol. 74(2), pages 714-724, June.
    3. Timothy D. Johnson, 2007. "Analysis of Pulsatile Hormone Concentration Profiles with Nonconstant Basal Concentration: A Bayesian Approach," Biometrics, The International Biometric Society, vol. 63(4), pages 1207-1217, December.
    4. Yu-Chieh Yang & Anna Liu & Yuedong Wang, 2006. "Detecting Pulsatile Hormone Secretions Using Nonlinear Mixed Effects Partial Spline Models," Biometrics, The International Biometric Society, vol. 62(1), pages 230-238, March.
    5. Rose T Faghih & Munther A Dahleh & Gail K Adler & Elizabeth B Klerman & Emery N Brown, 2014. "Deconvolution of Serum Cortisol Levels by Using Compressed Sensing," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
    6. Nichole E. Carlson & Timothy D. Johnson & Morton B. Brown, 2009. "A Bayesian Approach to Modeling Associations Between Pulsatile Hormones," Biometrics, The International Biometric Society, vol. 65(2), pages 650-659, June.
    7. Anna Liu & Yuedong Wang, 2007. "Modeling of Hormone Secretion-Generating Mechanisms with Splines: A Pseudo-Likelihood Approach," Biometrics, The International Biometric Society, vol. 63(1), pages 201-208, 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. Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
    2. 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.
    3. Arman Oganisian & Nandita Mitra & Jason A. Roy, 2021. "A Bayesian nonparametric model for zero‐inflated outcomes: Prediction, clustering, and causal estimation," Biometrics, The International Biometric Society, vol. 77(1), pages 125-135, March.
    4. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
    5. Rufo, M.J. & Pérez, C.J. & Martín, J., 2009. "Local parametric sensitivity for mixture models of lifetime distributions," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1238-1244.
    6. Jeong Eun Lee & Christian Robert, 2013. "Imortance Sampling Schemes for Evidence Approximation in Mixture Models," Working Papers 2013-42, Center for Research in Economics and Statistics.
    7. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Kiel Working Papers 1799, Kiel Institute for the World Economy (IfW Kiel).
    8. Sphiwe B. Skhosana & Salomon M. Millard & Frans H. J. Kanfer, 2023. "A Novel EM-Type Algorithm to Estimate Semi-Parametric Mixtures of Partially Linear Models," Mathematics, MDPI, vol. 11(5), pages 1-20, February.
    9. Sun-Joo Cho & Allan S. Cohen, 2010. "A Multilevel Mixture IRT Model With an Application to DIF," Journal of Educational and Behavioral Statistics, , vol. 35(3), pages 336-370, June.
    10. Ungolo, Francesco & Kleinow, Torsten & Macdonald, Angus S., 2020. "A hierarchical model for the joint mortality analysis of pension scheme data with missing covariates," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 68-84.
    11. Ioannis Ntzoufras & Claudia Tarantola, 2012. "Conjugate and Conditional Conjugate Bayesian Analysis of Discrete Graphical Models of Marginal Independence," Quaderni di Dipartimento 178, University of Pavia, Department of Economics and Quantitative Methods.
    12. Brian Hartley, 2020. "Corridor stability of the Kaleckian growth model: a Markov-switching approach," Working Papers 2013, New School for Social Research, Department of Economics, revised Nov 2020.
    13. Park, Byung-Jung & Zhang, Yunlong & Lord, Dominique, 2010. "Bayesian mixture modeling approach to account for heterogeneity in speed data," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 662-673, June.
    14. Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
    15. Simen Alexander Linge Johnsen & Jörg Bollmann, 2020. "Coccolith mass and morphology of different Emiliania huxleyi morphotypes: A critical examination using Canary Islands material," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-29, March.
    16. Nichole E. Carlson & Timothy D. Johnson & Morton B. Brown, 2009. "A Bayesian Approach to Modeling Associations Between Pulsatile Hormones," Biometrics, The International Biometric Society, vol. 65(2), pages 650-659, June.
    17. M. Rufo & J. Martín & C. Pérez, 2006. "Bayesian analysis of finite mixture models of distributions from exponential families," Computational Statistics, Springer, vol. 21(3), pages 621-637, December.
    18. Christian Aßmann, 2015. "Rossi, Peter E.: Bayesian non- and semi-parametric methods and applications," Journal of Economics, Springer, vol. 115(2), pages 195-197, June.
    19. Montanari, Angela & Viroli, Cinzia, 2011. "Maximum likelihood estimation of mixtures of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2712-2723, September.
    20. Alessio Farcomeni & Antonio Punzo, 2020. "Robust model-based clustering with mild and gross outliers," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 989-1007, December.

    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:bla:biomet:v:59:y:2003:i:3:p:650-660. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.