IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i15p2704-d876577.html
   My bibliography  Save this article

High-Dimensional Statistics: Non-Parametric Generalized Functional Partially Linear Single-Index Model

Author

Listed:
  • Mohamed Alahiane

    (Ecole Nationale des Sciences Appliquées, Université Cadi Ayyad, Marrakech 40000, Morocco)

  • Idir Ouassou

    (Ecole Nationale des Sciences Appliquées, Université Cadi Ayyad, Marrakech 40000, Morocco)

  • Mustapha Rachdi

    (Laboratoire AGEIS EA 7407, Université Grenoble Alpes, AGIM Team, UFR SHS, BP. 47, CEDEX 09, 38040 Grenoble, France)

  • Philippe Vieu

    (Institut de Mathématiques de Toulouse, Université Paul Sabatier, CEDEX 09, 31062 Toulouse, France)

Abstract

We study the non-parametric estimation of partially linear generalized single-index functional models, where the systematic component of the model has a flexible functional semi-parametric form with a general link function. We suggest an efficient and practical approach to estimate (I) the single-index link function, (II) the single-index coefficients as well as (III) the non-parametric functional component of the model. The estimation procedure is developed by applying quasi-likelihood, polynomial splines and kernel smoothings. We then derive the asymptotic properties, with rates, of the estimators of each component of the model. Their asymptotic normality is also established. By making use of the splines approximation and the Fisher scoring algorithm, we show that our approach has numerical advantages in terms of the practical efficiency and the computational stability. A computational study on data is provided to illustrate the good practical behavior of our methodology.

Suggested Citation

  • Mohamed Alahiane & Idir Ouassou & Mustapha Rachdi & Philippe Vieu, 2022. "High-Dimensional Statistics: Non-Parametric Generalized Functional Partially Linear Single-Index Model," Mathematics, MDPI, vol. 10(15), pages 1-21, July.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2704-:d:876577
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/15/2704/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/15/2704/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu Y. & Ruppert D., 2002. "Penalized Spline Estimation for Partially Linear Single-Index Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1042-1054, December.
    2. Germán Aneiros & Philippe Vieu, 2015. "Partial linear modelling with multi-functional covariates," Computational Statistics, Springer, vol. 30(3), pages 647-671, September.
    3. Mohamed Alahiane & Idir Ouassou & Mustapha Rachdi & Philippe Vieu, 2021. "Partially Linear Generalized Single Index Models for Functional Data (PLGSIMF)," Stats, MDPI, vol. 4(4), pages 1-21, September.
    4. Chin-Shang Li & Minggen Lu, 2018. "A lack-of-fit test for generalized linear models via single-index techniques," Computational Statistics, Springer, vol. 33(2), pages 731-756, June.
    5. Ping Yu & Jiang Du & Zhongzhan Zhang, 2020. "Single-index partially functional linear regression model," Statistical Papers, Springer, vol. 61(3), pages 1107-1123, June.
    6. Idir Ouassou & Mustapha Rachdi, 2012. "Regression operator estimation by delta-sequences method for functional data and its applications," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(4), pages 451-465, October.
    7. Peng Lai & Ye Tian & Heng Lian, 2014. "Estimation and variable selection for generalised partially linear single-index models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 171-185, March.
    8. Ruiyuan Cao & Jiang Du & Jianjun Zhou & Tianfa Xie, 2020. "FPCA-based estimation for generalized functional partially linear models," Statistical Papers, Springer, vol. 61(6), pages 2715-2735, December.
    9. Aneiros-Pérez, Germán & Vieu, Philippe, 2006. "Semi-functional partial linear regression," Statistics & Probability Letters, Elsevier, vol. 76(11), pages 1102-1110, June.
    10. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(2), pages 186-199, June.
    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. Mohamed Alahiane & Idir Ouassou & Mustapha Rachdi & Philippe Vieu, 2021. "Partially Linear Generalized Single Index Models for Functional Data (PLGSIMF)," Stats, MDPI, vol. 4(4), pages 1-21, September.
    2. Mustapha Rachdi & Mohamed Alahiane & Idir Ouassou & Abdelaziz Alahiane & Lahoucine Hobbad, 2024. "Cross-Validated Functional Generalized Partially Linear Single-Functional Index Model," Mathematics, MDPI, vol. 12(17), pages 1-22, August.
    3. Ping Yu & Jiang Du & Zhongzhan Zhang, 2020. "Single-index partially functional linear regression model," Statistical Papers, Springer, vol. 61(3), pages 1107-1123, June.
    4. Ruiyuan Cao & Jiang Du & Jianjun Zhou & Tianfa Xie, 2020. "FPCA-based estimation for generalized functional partially linear models," Statistical Papers, Springer, vol. 61(6), pages 2715-2735, December.
    5. Shuyu Meng & Zhensheng Huang, 2024. "Variable Selection in Semi-Functional Partially Linear Regression Models with Time Series Data," Mathematics, MDPI, vol. 12(17), pages 1-23, September.
    6. Aneiros, Germán & Cao, Ricardo & Fraiman, Ricardo & Genest, Christian & Vieu, Philippe, 2019. "Recent advances in functional data analysis and high-dimensional statistics," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 3-9.
    7. Shuzhi Zhu & Peixin Zhao, 2019. "Tests for the linear hypothesis in semi-functional partial linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 125-148, March.
    8. Slaoui, Yousri, 2019. "Wild bootstrap bandwidth selection of recursive nonparametric relative regression for independent functional data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 494-511.
    9. Vieu, Philippe, 2018. "On dimension reduction models for functional data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 134-138.
    10. Ping Yu & Zhongyi Zhu & Zhongzhan Zhang, 2019. "Robust exponential squared loss-based estimation in semi-functional linear regression models," Computational Statistics, Springer, vol. 34(2), pages 503-525, June.
    11. Belli, Edoardo, 2022. "Smoothly adaptively centered ridge estimator," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    12. Wu, Chaojiang & Yu, Yan, 2014. "Partially linear modeling of conditional quantiles using penalized splines," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 170-187.
    13. Bin Yang & Min Chen & Tong Su & Jianjun Zhou, 2023. "Robust Estimation for Semi-Functional Linear Model with Autoregressive Errors," Mathematics, MDPI, vol. 11(2), pages 1-14, January.
    14. Lydia Kara-Zaitri & Ali Laksaci & Mustapha Rachdi & Philippe Vieu, 2017. "Uniform in bandwidth consistency for various kernel estimators involving functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(1), pages 85-107, January.
    15. Feng, Long & Zou, Changliang & Wang, Zhaojun, 2012. "Rank-based inference for the single-index model," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 535-541.
    16. Boumahdi, Mounir & Ouassou, Idir & Rachdi, Mustapha, 2023. "Estimation in nonparametric functional-on-functional models with surrogate responses," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
    17. Usset, Joseph & Staicu, Ana-Maria & Maity, Arnab, 2016. "Interaction models for functional regression," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 317-329.
    18. Ahmedou, Aziza & Marion, Jean-Marie & Pumo, Besnik, 2016. "Generalized linear model with functional predictors and their derivatives," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 313-324.
    19. Holland, Ashley D., 2017. "Penalized spline estimation in the partially linear model," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 211-235.
    20. Rong Jiang & Wei-Min Qian & Zhan-Gong Zhou, 2016. "Single-index composite quantile regression with heteroscedasticity and general error distributions," Statistical Papers, Springer, vol. 57(1), pages 185-203, March.

    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:gam:jmathe:v:10:y:2022:i:15:p:2704-:d:876577. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.