IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v59y2003i4p984-991.html
   My bibliography  Save this article

Using Local Correlation in Kernel-Based Smoothers for Dependent Data

Author

Listed:
  • Derick R. Peterson
  • Hongwei Zhao
  • Sara Eapen

Abstract

No abstract is available for this item.

Suggested Citation

  • Derick R. Peterson & Hongwei Zhao & Sara Eapen, 2003. "Using Local Correlation in Kernel-Based Smoothers for Dependent Data," Biometrics, The International Biometric Society, vol. 59(4), pages 984-991, December.
    • Handle: RePEc:bla:biomet:v:59:y:2003:i:4:p:984-991
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2003.00113.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. Yuedong Wang, 1998. "Mixed effects smoothing spline analysis of variance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(1), pages 159-174.
    2. Arũnas P. Verbyla & Brian R. Cullis & Michael G. Kenward & Sue J. Welham, 1999. "The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 269-311.
    3. Kjell Doksum & Derick Peterson & Alex Samarov, 2000. "On variable bandwidth selection in local polynomial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 431-448.
    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. Welham, S.J. & Thompson, R., 2009. "A note on bimodality in the log-likelihood function for penalized spline mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 920-931, February.
    2. M. P. Wand, 2003. "Smoothing and mixed models," Computational Statistics, Springer, vol. 18(2), pages 223-249, July.
    3. Faustin Habyarimana & Shaun Ramroop, 2015. "Determinants of Poverty of Households: Semi parametric Analysis of Demographic and Health Survey Data from Rwanda," Journal of Economics and Behavioral Studies, AMH International, vol. 7(3), pages 47-55.
    4. Lihui Zhao & Tom Chen & Vladimir Novitsky & Rui Wang, 2021. "Joint penalized spline modeling of multivariate longitudinal data, with application to HIV‐1 RNA load levels and CD4 cell counts," Biometrics, The International Biometric Society, vol. 77(3), pages 1061-1074, September.
    5. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    6. Lee, Dae-Jin & Durbán, María, 2009. "P-spline anova-type interaction models for spatio-temporal smoothing," DES - Working Papers. Statistics and Econometrics. WS ws093312, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Jaroslaw Harezlak & Louise M. Ryan & Jay N. Giedd & Nicholas Lange, 2005. "Individual and Population Penalized Regression Splines for Accelerated Longitudinal Designs," Biometrics, The International Biometric Society, vol. 61(4), pages 1037-1048, December.
    8. Arūnas P. Verbyla & Joanne Faveri & John D. Wilkie & Tom Lewis, 2018. "Tensor Cubic Smoothing Splines in Designed Experiments Requiring Residual Modelling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 478-508, December.
    9. Woojoo Lee & Hans‐Peter Piepho & Youngjo Lee, 2021. "Resolving the ambiguity of random‐effects models with singular precision matrix," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 75(4), pages 482-499, November.
    10. Xiao Ni & Daowen Zhang & Hao Helen Zhang, 2010. "Variable Selection for Semiparametric Mixed Models in Longitudinal Studies," Biometrics, The International Biometric Society, vol. 66(1), pages 79-88, March.
    11. Tianxi Cai & Rebecca A. Betensky, 2003. "Hazard Regression for Interval-Censored Data with Penalized Spline," Biometrics, The International Biometric Society, vol. 59(3), pages 570-579, September.
    12. Göran Kauermann & Tatyana Krivobokova & Ludwig Fahrmeir, 2009. "Some asymptotic results on generalized penalized spline smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 487-503, April.
    13. Leontine Alkema & Jin Rou New & Jon Pedersen & Danzhen You & all members of the UN Inter-agency Group for Child Mortality Estimation and its Technical Advisory Group, 2014. "Child Mortality Estimation 2013: An Overview of Updates in Estimation Methods by the United Nations Inter-Agency Group for Child Mortality Estimation," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
    14. Ruixue Du & Hiroshi Yamada, 2020. "Principle of Duality in Cubic Smoothing Spline," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    15. Lee, Dae-Jin & Durbán, María, 2008. "Smooth-car mixed models for spatial count data," DES - Working Papers. Statistics and Econometrics. WS ws085820, Universidad Carlos III de Madrid. Departamento de Estadística.
    16. Lauren N. Berry & Nathaniel E. Helwig, 2021. "Cross-Validation, Information Theory, or Maximum Likelihood? A Comparison of Tuning Methods for Penalized Splines," Stats, MDPI, vol. 4(3), pages 1-24, September.
    17. Beran, Jan & Liu, Haiyan, 2016. "Estimation of eigenvalues, eigenvectors and scores in FDA models with dependent errors," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 218-233.
    18. Rafael A. Irizarry & Clarke Tankersley & Robert Frank & Susan Flanders, 2001. "Assessing Homeostasis Through Circadian Patterns," Biometrics, The International Biometric Society, vol. 57(4), pages 1228-1237, December.
    19. Philipp F. M. Baumann & Enzo Rossi & Alexander Volkmann, 2020. "What Drives Inflation and How: Evidence from Additive Mixed Models Selected by cAIC," Papers 2006.06274, arXiv.org, revised Aug 2022.
    20. Dale Zimmerman & Vicente Núñez-Antón & Timothy Gregoire & Oliver Schabenberger & Jeffrey Hart & Michael Kenward & Geert Molenberghs & Geert Verbeke & Mohsen Pourahmadi & Philippe Vieu & Dela Zimmerman, 2001. "Parametric modelling of growth curve data: An overview," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(1), pages 1-73, 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:bla:biomet:v:59:y:2003:i:4:p:984-991. 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.