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A class of functional partially linear single-index models

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  • Ding, Hui
  • Liu, Yanghui
  • Xu, Wenchao
  • Zhang, Riquan

Abstract

The functional linear regression model is a useful extension of the classical linear model. However, it assumes a linear relationship between the response and functional covariates which may be invalid. For this reason, we generalize this model to a class of functional partially linear single-index models. In this paper, we propose a profile least squares approach combined with local constant smoothing for estimating the slope function and the link function in the new model. We demonstrate that our methods enable prediction of the link function and estimation of the slope function with polynomial convergence rates. The convergence rate of prediction of the whole model is also established. Monte Carlo simulation studies show an excellent finite-sample performance. A real data example about average yield of oats in Saskatchewan, Canada is used to illustrate our methodology.

Suggested Citation

  • Ding, Hui & Liu, Yanghui & Xu, Wenchao & Zhang, Riquan, 2017. "A class of functional partially linear single-index models," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 68-82.
    • Handle: RePEc:eee:jmvana:v:161:y:2017:i:c:p:68-82
    DOI: 10.1016/j.jmva.2017.07.004
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    References listed on IDEAS

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    1. Cardot, Hervé & Ferraty, Frédéric & Sarda, Pascal, 1999. "Functional linear model," Statistics & Probability Letters, Elsevier, vol. 45(1), pages 11-22, October.
    2. Xia, Yingcun & Härdle, Wolfgang, 2006. "Semi-parametric estimation of partially linear single-index models," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1162-1184, May.
    3. Li, Yehua & Hsing, Tailen, 2007. "On rates of convergence in functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 98(9), pages 1782-1804, October.
    4. Wang, Qihua & Xue, Liugen, 2011. "Statistical inference in partially-varying-coefficient single-index model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 1-19, January.
    5. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    6. Shin, Hyejin & Lee, Myung Hee, 2012. "On prediction rate in partial functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 93-106, January.
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    Cited by:

    1. Nengxiang Ling & Lilei Cheng & Philippe Vieu & Hui Ding, 2022. "Missing responses at random in functional single index model for time series data," Statistical Papers, Springer, vol. 63(2), pages 665-692, April.
    2. Liebl, Dominik & Walders, Fabian, 2019. "Parameter regimes in partial functional panel regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 105-115.
    3. Silvia Novo & Germán Aneiros & Philippe Vieu, 2021. "Sparse semiparametric regression when predictors are mixture of functional and high-dimensional variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(2), pages 481-504, June.

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