IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v63y2007i4p1269-1277.html
   My bibliography  Save this article

Penalized Item Response Theory Models: Application to Epigenetic Alterations in Bladder Cancer

Author

Listed:
  • E. Andrés Houseman
  • Carmen Marsit
  • Margaret Karagas
  • Louise M. Ryan

Abstract

No abstract is available for this item.

Suggested Citation

  • E. Andrés Houseman & Carmen Marsit & Margaret Karagas & Louise M. Ryan, 2007. "Penalized Item Response Theory Models: Application to Epigenetic Alterations in Bladder Cancer," Biometrics, The International Biometric Society, vol. 63(4), pages 1269-1277, December.
    • Handle: RePEc:bla:biomet:v:63:y:2007:i:4:p:1269-1277
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00806.x
    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. C. Y. Wang & Naisyin Wang & Suojin Wang, 2000. "Regression Analysis When Covariates Are Regression Parameters of a Random Effects Model for Observed Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 56(2), pages 487-495, June.
    2. E. Andrés Houseman & Brent A. Coull & Rebecca A. Betensky, 2006. "Feature-Specific Penalized Latent Class Analysis for Genomic Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1062-1070, December.
    3. Sanchez, Brisa N. & Budtz-Jorgensen, Esben & Ryan, Louise M. & Hu, Howard, 2005. "Structural Equation Models: A Review With Applications to Environmental Epidemiology," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1443-1455, December.
    4. Klaus Larsen, 2005. "The Cox Proportional Hazards Model with a Continuous Latent Variable Measured by Multiple Binary Indicators," Biometrics, The International Biometric Society, vol. 61(4), pages 1049-1055, December.
    5. Shelley A. Blozis & Robert Cudeck, 1999. "Conditionally Linear Mixed-Effects Models With Latent Variable Covariates," Journal of Educational and Behavioral Statistics, , vol. 24(3), pages 245-270, September.
    6. Raymond J. Adams & Mark Wilson & Margaret Wu, 1997. "Multilevel Item Response Models: An Approach to Errors in Variables Regression," Journal of Educational and Behavioral Statistics, , vol. 22(1), pages 47-76, March.
    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. Samuel Iddi & Geert Molenberghs, 2012. "A joint marginalized multilevel model for longitudinal outcomes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2413-2430, July.
    2. Brent A Coull, 2011. "A Random Intercepts–Functional Slopes Model for Flexible Assessment of Susceptibility in Longitudinal Designs," Biometrics, The International Biometric Society, vol. 67(2), pages 486-494, June.
    3. Meenakshi Rao & Linda A. George & Vivek Shandas & Todd N. Rosenstiel, 2017. "Assessing the Potential of Land Use Modification to Mitigate Ambient NO 2 and Its Consequences for Respiratory Health," IJERPH, MDPI, vol. 14(7), pages 1-19, July.
    4. Baolin Wu, 2013. "Sparse cluster analysis of large-scale discrete variables with application to single nucleotide polymorphism data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 358-367, February.
    5. Klaus Holst & Esben Budtz-Jørgensen, 2013. "Linear latent variable models: the lava-package," Computational Statistics, Springer, vol. 28(4), pages 1385-1452, August.
    6. Ching‐Yun Wang & Xiao Song, 2021. "Semiparametric regression calibration for general hazard models in survival analysis with covariate measurement error; surprising performance under linear hazard," Biometrics, The International Biometric Society, vol. 77(2), pages 561-572, June.
    7. Erning Li & Naisyin Wang & Nae-Yuh Wang, 2007. "Joint Models for a Primary Endpoint and Multiple Longitudinal Covariate Processes," Biometrics, The International Biometric Society, vol. 63(4), pages 1068-1078, December.
    8. D. B. Woodard & T. M. T. Love & S. W. Thurston & D. Ruppert & S. Sathyanarayana & S. H. Swan, 2013. "Latent factor regression models for grouped outcomes," Biometrics, The International Biometric Society, vol. 69(3), pages 785-794, September.
    9. Amy LaLonde & Tanzy Love & Sally W. Thurston & Philip W. Davidson, 2020. "Discovering structure in multiple outcomes models for tests of childhood neurodevelopment," Biometrics, The International Biometric Society, vol. 76(3), pages 874-885, September.
    10. Song, Xin-Yuan & Tang, Nian-Sheng & Chow, Sy-Miin, 2012. "A Bayesian approach for generalized random coefficient structural equation models for longitudinal data with adjacent time effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4190-4203.
    11. Duchwan Ryu & Erning Li & Bani K. Mallick, 2011. "Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 67(2), pages 454-466, June.
    12. Xiao Song & C. Y. Wang, 2008. "Semiparametric Approaches for Joint Modeling of Longitudinal and Survival Data with Time-Varying Coefficients," Biometrics, The International Biometric Society, vol. 64(2), pages 557-566, June.
    13. Vinícius Diniz Mayrink & Renato Valladares Panaro & Marcelo Azevedo Costa, 2021. "Structural equation modeling with time dependence: an application comparing Brazilian energy distributors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 353-383, June.
    14. Sally W. Thurston & David Ruppert & Philip W. Davidson, 2009. "Bayesian Models for Multiple Outcomes Nested in Domains," Biometrics, The International Biometric Society, vol. 65(4), pages 1078-1086, December.
    15. Kamble, Sachin S. & Gunasekaran, Angappa & Kumar, Vikas & Belhadi, Amine & Foropon, Cyril, 2021. "A machine learning based approach for predicting blockchain adoption in supply Chain," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    16. Xianzheng Huang & Leonard A. Stefanski & Marie Davidian, 2009. "Latent-Model Robustness in Joint Models for a Primary Endpoint and a Longitudinal Process," Biometrics, The International Biometric Society, vol. 65(3), pages 719-727, September.
    17. Ching-Yun Wang & Jean de Dieu Tapsoba & Catherine Duggan & Anne McTiernan, 2024. "Generalized Linear Models with Covariate Measurement Error and Zero-Inflated Surrogates," Mathematics, MDPI, vol. 12(2), pages 1-14, January.
    18. Bernhardt, Paul W. & Zhang, Daowen & Wang, Huixia Judy, 2015. "A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 37-53.
    19. Norman Rose & Matthias Davier & Benjamin Nagengast, 2017. "Modeling Omitted and Not-Reached Items in IRT Models," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 795-819, September.
    20. Bonnie E. Shook-Sa & Ding-Geng Chen & Haibo Zhou, 2017. "Using Structural Equation Modeling to Assess the Links between Tobacco Smoke Exposure, Volatile Organic Compounds, and Respiratory Function for Adolescents Aged 6 to 18 in the United States," IJERPH, MDPI, vol. 14(10), pages 1-12, September.

    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:63:y:2007:i:4:p:1269-1277. 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.