IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v50y2006i7p1818-1839.html
   My bibliography  Save this article

On fitting generalized non-linear models with varying coefficients

Author

Listed:
  • Staniswalis, Joan G.

Abstract

No abstract is available for this item.

Suggested Citation

  • Staniswalis, Joan G., 2006. "On fitting generalized non-linear models with varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1818-1839, April.
    • Handle: RePEc:eee:csdana:v:50:y:2006:i:7:p:1818-1839
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(05)00040-X
    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. Jianqing Fan & Qiwei Yao & Zongwu Cai, 2003. "Adaptive varying‐coefficient linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 57-80, February.
    2. Andrew Gelman & David K. Park & Stephen Ansolabehere & Phillip N. Price & Lorraine C. Minnite, 2001. "Models, assumptions and model checking in ecological regressions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 101-118.
    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. Joan G. Staniswalis, 2008. "Incorporating Marginal Covariate Information in a Nonparametric Regression Model for a Sample of R×C Tables," Biometrics, The International Biometric Society, vol. 64(4), pages 1054-1061, December.

    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. Aiyar, Anaka & Cummins, Joseph R., 2021. "An age profile perspective on two puzzles in global child health: The Indian Enigma & economic growth," Journal of Development Economics, Elsevier, vol. 148(C).
    2. Jianhong Shi & Qian Yang & Xiongya Li & Weixing Song, 2017. "Effects of measurement error on a class of single-index varying coefficient regression models," Computational Statistics, Springer, vol. 32(3), pages 977-1001, September.
    3. Wong, Heung & Ip, Wai-cheung & Zhang, Riquan, 2008. "Varying-coefficient single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1458-1476, January.
    4. Xue, Yuan & Yin, Xiangrong & Jiang, Xiaolin, 2016. "Ensemble sufficient dimension folding methods for analyzing matrix-valued data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 193-205.
    5. J. Q. Shi & B. Wang & R. Murray-Smith & D. M. Titterington, 2007. "Gaussian Process Functional Regression Modeling for Batch Data," Biometrics, The International Biometric Society, vol. 63(3), pages 714-723, September.
    6. Čížek, Pavel & Koo, Chao Hui, 2021. "Jump-preserving varying-coefficient models for nonlinear time series," Econometrics and Statistics, Elsevier, vol. 19(C), pages 58-96.
    7. Jose Olmo, 2023. "A nonparametric predictive regression model using partitioning estimators based on Taylor expansions," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 294-318, May.
    8. Cai, Zongwu & Xu, Xiaoping, 2009. "Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 371-383.
    9. Matthias Fengler & Wolfgang Härdle & Enno Mammen, 2005. "A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics," SFB 649 Discussion Papers SFB649DP2005-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Hai‐Bin Wang, 2008. "Nonlinear ARMA models with functional MA coefficients," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(6), pages 1032-1056, November.
    11. Breunig, Christoph, 2021. "Varying random coefficient models," Journal of Econometrics, Elsevier, vol. 221(2), pages 381-408.
    12. Marta Benet & Patricia Celi-Medina & Montserrat Fernández & Sandra Ezquerra, 2022. "The COVID-19 Pandemic in Care Homes: An Exploration of Its Impact across Regions in Spain," IJERPH, MDPI, vol. 19(15), pages 1-16, August.
    13. Xialu Liu & Zongwu Cai & Rong Chen, 2015. "Functional coefficient seasonal time series models with an application of Hawaii tourism data," Computational Statistics, Springer, vol. 30(3), pages 719-744, September.
    14. Giancarlo Bruno, 2014. "Consumer confidence and consumption forecast: a non-parametric approach," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 37-52, February.
    15. Kim, Young-Ju, 2013. "A partial spline approach for semiparametric estimation of varying-coefficient partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 181-187.
    16. Bernard Grofman & Matt A. Barreto, 2009. "A Reply to Zax's (2002) Critique of Grofman and Migalski (1988)," Sociological Methods & Research, , vol. 37(4), pages 599-617, May.
    17. Harvill, Jane L. & Ray, Bonnie K., 2006. "Functional coefficient autoregressive models for vector time series," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3547-3566, August.
    18. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    19. Yang, Hu & Guo, Chaohui & Lv, Jing, 2014. "A robust and efficient estimation method for single-index varying-coefficient models," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 119-127.
    20. Park, Byeong U. & Mammen, Enno & Härdle, Wolfgang & Borak, Szymon, 2009. "Time Series Modelling With Semiparametric Factor Dynamics," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 284-298.

    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:50:y:2006:i:7:p:1818-1839. 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.