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Kernel density estimation for partial linear multivariate responses models

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  • Zhang, Jun
  • Lin, Bingqing
  • Zhou, Yan

Abstract

We propose a kernel density based estimation by constructing a nonparametric kernel version of the maximum profile likelihood estimator for partial linear multivariate responses regression models. The method proposed in this article makes use of multivariate kernel smoothing nonparametric techniques to estimate the unknown multivariate density function. For the hypothesis testing of parametric components, restricted estimators under the null hypothesis and test statistics are proposed. The asymptotic properties for the estimators and test statistics are established. We illustrate our proposals through simulations and an analysis of the energy efficiency data. Our analysis provides strong evidence that the proposed kernel density based estimator is superior than the profile least squares estimator, particularly for multimodal or heavy-tailed distributions of the model errors.

Suggested Citation

  • Zhang, Jun & Lin, Bingqing & Zhou, Yan, 2021. "Kernel density estimation for partial linear multivariate responses models," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:jmvana:v:185:y:2021:i:c:s0047259x21000464
    DOI: 10.1016/j.jmva.2021.104768
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    References listed on IDEAS

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    1. Zonghui Hu, 2004. "Profile-kernel versus backfitting in the partially linear models for longitudinal/clustered data," Biometrika, Biometrika Trust, vol. 91(2), pages 251-262, June.
    2. 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.
    3. Chang, Y. C., 1985. "A note on the covariance matrix of the maximum likelihood estimator in constrained multivariate linear regression," Journal of Econometrics, Elsevier, vol. 28(2), pages 247-252, May.
    4. Hua Liang & Yongsong Qin & Xinyu Zhang & David Ruppert, 2009. "Empirical Likelihood‐Based Inferences for Generalized Partially Linear Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 433-443, September.
    5. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    6. Ao Yuan & Jan G. De Gooijer, 2007. "Semiparametric Regression with Kernel Error Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 841-869, December.
    7. Jun Zhang & Zhenghui Feng & Peirong Xu, 2015. "Estimating the conditional single-index error distribution with a partial linear mean regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 61-83, March.
    8. Ao Yuan, 2009. "Semiparametric inference with kernel likelihood," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(2), pages 207-228.
    9. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    10. Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-1092, November.
    11. Varadhan, Ravi & Gilbert, Paul, 2009. "BB: An R Package for Solving a Large System of Nonlinear Equations and for Optimizing a High-Dimensional Nonlinear Objective Function," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i04).
    12. Leo Breiman & Jerome H. Friedman, 1997. "Predicting Multivariate Responses in Multiple Linear Regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 3-54.
    13. Xihong Lin & Raymond J. Carroll, 2006. "Semiparametric estimation in general repeated measures problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 69-88, February.
    14. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
    15. Li, Gaorong & Feng, Sanying & Peng, Heng, 2011. "A profile-type smoothed score function for a varying coefficient partially linear model," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 372-385, February.
    16. Heng Lian, 2020. "Asymptotics of the Non‐parametric Function for B‐splines‐based Estimation in Partially Linear Models," International Statistical Review, International Statistical Institute, vol. 88(1), pages 142-154, April.
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    Cited by:

    1. Facheng Li & Huilan Liu, 2025. "Kernel density regression in the additive model: a B-spline approach," Statistical Papers, Springer, vol. 66(1), pages 1-23, February.

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