58
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Statistical inference for the extended non linear models

, &
Received 16 Oct 2023, Accepted 04 Mar 2024, Published online: 18 Mar 2024

References

  • Cui, X., W. Guo, L. Lin, and L. X. Zhu. 2009. Covariate-adjusted nonlinear regression. The Annals of Statistics 37 (4):1839–70.
  • Hamilton, S. A, and Y. K. Truong. 1997. Local linear estimation in partlylinear models. Journal of Multivariate Analysis 60 (1):1–19. doi:10.1006/jmva.1996.1642.
  • Härdle, W., H. Liang, and J. Gao. 2000. Partially linear models. Heidelberg: Physica-Verlag.
  • Lai, P., Q. Wang, and X.-H. Zhou. 2014. Variable selection and semiparametric efficient estimation for the heteroscedastic partially linear single-index model. Computational Statistics & Data Analysis 70:241–56. doi:10.1016/j.csda.2013.09.012.
  • Li, G., L. Zhu, L. Xue, and S. Feng. 2010. Empirical likelihood inference in partially linear single-index models for longitudinal data. Journal of Multivariate Analysis 101 (3):718–32. doi:10.1016/j.jmva.2009.08.006.
  • Lian, H., Hua. Liang, and R. J. Carroll. 2015. Variance function partially linear single-index models1. Journal of the Royal Statistical Society. Series B, Statistical Methodology 77 (1):171–94. doi:10.1111/rssb.12066. 25642139.
  • Liang, H., X. Liu, R. Z. Li, and C. L. Tsai. 2010. Estimation and testing for partially linear single-index models. The Annals of Statistics 38:3811–36.
  • Liang, H., H. Wang, and C. L. Tsai. 2012. Profiled forward regression for ultrahigh dimensional variable screening in semiparametric partially linear models. Statistica Sinica 22 (2):531–54.
  • Lovric, M. 2011. International encyclopedia of statistical science. Berlin/Heidelberg: Springer.
  • Nolan, J. P., and D. Ojeda-Revah. 2013. Linear and nonlinear regression with stable errors. Journal of Econometrics 172 (2):186–94. doi:10.1016/j.jeconom.2012.08.008.
  • Wang, J. L., L. G. Xue, L. X. Zhu, and Y. S. Chong. 2010. Estimation for a partial-linear single-index model. The Annals of Statistics 38:246–74.
  • Wang, X., Y. Jiang, M. Huang, and H. Zhang. 2013. Robust variable selection with exponential squared loss. Journal of the American Statistical Association 108 (502):632–43. doi:10.1080/01621459.2013.766613. 23913996.
  • Wu, C.-F. 1981. Asymptotic theory of nonlinear least squares estimation. The Annals of Statistics 9 (3):501–13.
  • Wu, P., and L. X. Zhu. 2010. An orthogonality-based estimation of moments for linear mixed models. Scandinavian Journal of Statistics 37 (2):253–63. doi:10.1111/j.1467-9469.2009.00673.x.
  • Yang, Y., G. Li, and H. Lian. 2016. Nonconcave penalized estimation for partially linear models with longitudinal data. Statistics 50 (1):43–59. doi:10.1080/02331888.2015.1074232.
  • Zhang, J., and Y. Gai. 2022. Nonlinear regression models with profile nonlinear least squares estimation. Communications in Statistics - Simulation and Computation 51 (5):2140–57. doi:10.1080/03610918.2019.1700280.
  • Zhang, J., B. Lin, and G. Li. 2019. Nonlinear regression models with general distortion measurement errors. Journal of Statistical Computation and Simulation 89 (8):1482–504. doi:10.1080/00949655.2019.1586904.
  • Zhu, L., and L. Xue. 2006. Empirical likelihood confidence regions in a partially linear single-index model. Journal of the Royal Statistical Society Series B: Statistical Methodology 68 (3):549–70. doi:10.1111/j.1467-9868.2006.00556.x.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.