IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v77y2007i15p1580-1588.html

Estimation of generalized partially linear models with measurement error using sufficiency scores

Author

Listed:
  • Liu, Lian

Abstract

We study the partially linear model in logistic and other types of canonical exponential family regression when the explanatory variable is measured with independent normal error. We develop a backfitting estimation procedure to this model based upon the parametric idea of sufficiency scores so that no assumptions are made about the latent variable measured with error. We derive the method's asymptotic properties and present a numerical example and a simulation study.

Suggested Citation

  • Liu, Lian, 2007. "Estimation of generalized partially linear models with measurement error using sufficiency scores," Statistics & Probability Letters, Elsevier, vol. 77(15), pages 1580-1588, September.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:15:p:1580-1588
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(07)00134-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Maity, Arnab & Ma, Yanyuan & Carroll, Raymond J., 2007. "Efficient Estimation of Population-Level Summaries in General Semiparametric Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 123-139, March.
    2. Gerda Claeskens & Raymond J. Carroll, 2007. "An asymptotic theory for model selection inference in general semiparametric problems," Biometrika, Biometrika Trust, vol. 94(2), pages 249-265.
    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. Qianqian Wang & Yanyuan Ma & Guangren Yang, 2020. "Locally efficient estimation in generalized partially linear model with measurement error in nonlinear function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 553-572, June.

    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. Maity, Arnab, 2008. "Efficient estimation of population quantiles in general semiparametric regression models," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2744-2750, November.
    2. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    3. Haili Zhang & Guohua Zou, 2020. "Cross-Validation Model Averaging for Generalized Functional Linear Model," Econometrics, MDPI, vol. 8(1), pages 1-35, February.
    4. Hai Wang & Xinjie Chen & Nancy Flournoy, 2016. "The focused information criterion for varying-coefficient partially linear measurement error models," Statistical Papers, Springer, vol. 57(1), pages 99-113, March.
    5. Toru Kitagawa & Chris Muris, 2013. "Covariate selection and model averaging in semiparametric estimation of treatment effects," CeMMAP working papers 61/13, Institute for Fiscal Studies.
    6. S. Hossain & S. Ejaz Ahmed & Grace Y. Yi & B. Chen, 2016. "Shrinkage and pretest estimators for longitudinal data analysis under partially linear models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(3), pages 531-549, September.
    7. Wei, Jiawei & Carroll, Raymond J. & Maity, Arnab, 2011. "Testing for constant nonparametric effects in general semiparametric regression models with interactions," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 717-723, July.
    8. Tao Huang & Jialiang Li, 2018. "Semiparametric model average prediction in panel data analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 125-144, January.
    9. Ruoyao Shi & Zhipeng Liao, 2018. "An Averaging GMM Estimator Robust to Misspecification," Working Papers 201803, University of California at Riverside, Department of Economics.
    10. Zhou, Ling & Lin, Huazhen & Chen, Kani & Liang, Hua, 2019. "Efficient estimation and computation of parameters and nonparametric functions in generalized semi/non-parametric regression models," Journal of Econometrics, Elsevier, vol. 213(2), pages 593-607.
    11. Xiao‐Hua Zhou & Huazhen Lin & Eric Johnson, 2008. "Non‐parametric heteroscedastic transformation regression models for skewed data with an application to health care costs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 1029-1047, November.
    12. S. C. Pandhare & T. V. Ramanathan, 2020. "The robust focused information criterion for strong mixing stochastic processes with $$\mathscr {L}^{2}$$ L 2 -differentiable parametric densities," Statistical Inference for Stochastic Processes, Springer, vol. 23(3), pages 637-663, October.
    13. Liu, Chu-An, 2015. "Distribution theory of the least squares averaging estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 142-159.
    14. Xu Cheng & Zhipeng Liao & Ruoyao Shi, 2013. "Uniform Asymptotic Risk of Averaging GMM Estimator Robust to Misspecification, Second Version," PIER Working Paper Archive 15-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 25 Mar 2015.
    15. Jiang Du & Zhongzhan Zhang & Tianfa Xie, 2017. "Focused information criterion and model averaging in censored quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(5), pages 547-570, July.
    16. Shou-Yung Yin & Chu-An Liu & Chang-Ching Lin, 2021. "Focused Information Criterion and Model Averaging for Large Panels With a Multifactor Error Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 54-68, January.
    17. Gerda Claeskens, 2012. "Focused estimation and model averaging with penalization methods: an overview," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 272-287, August.
    18. Fang, Fang & Li, Jialiang & Xia, Xiaochao, 2022. "Semiparametric model averaging prediction for dichotomous response," Journal of Econometrics, Elsevier, vol. 229(2), pages 219-245.
    19. Häggström, Jenny, 2013. "Bandwidth selection for backfitting estimation of semiparametric additive models: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 136-148.
    20. S. C. Pandhare & T. V. Ramanathan, 2020. "The focussed information criterion for generalised linear regression models for time series," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 485-507, December.

    More about this item

    Keywords

    ;

    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:stapro:v:77:y:2007:i:15:p:1580-1588. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.