Identification for partially linear regression model with autoregressive errors

References

• Ruppert D, Wand MP, Carroll RJ. Semiparametric regression. New York: Cambridge University Press; 2003.
   Google Scholar
• Yatchew A. Semiparametric regression for the applied econometrician. New York: Cambridge University Press; 2003.
   Google Scholar
• Härdle W, Müller M, Sperlich S, et al. Nonparametric and semiparametric models. New York: Springer-Verlag;
   Google Scholar
• Opsomer JD, Ruppert D. A root-n consistent backfitting estimator for semiparametric additive modeling. J Comput Graph Stat. 1999;8:715–732.
   Web of Science ®Google Scholar
• Engle RF, Granger C, Weiss A. Semiparametric estimates of the relation between weather and electricity sales. J Am Stat Assoc. 1986;81:310–320.
   Web of Science ®Google Scholar
• Härdle W, Liang H, Gao JT. Partially linear models. Physica-Verlag: Heidelberg; 2000.
   Google Scholar
• Horowitz JL. Semiparametric and nonparametric methods in econometrics: Springer series in statistics. New York: Springer-Verlag; 2009.
   Google Scholar
• Liu X, Wang L, Liang H. Variable selection and estimation for semiparametric additive partial linear models. Stat Sin. 2011;21:1225–1248.
   PubMed Web of Science ®Google Scholar
• Roozbeh M, Arashi M, Gasparini M. Seemingly unrelated ridge regression in semiparametric models. Commun Stat Theory Methods. 2012;41(8):1364–1386.
   Web of Science ®Google Scholar
• Amini M, Roozbeh M. Least trimmed squares ridge estimation in partially linear regression models. J Stat Comput Simul. 2016;86(14):2766–2780.
   Web of Science ®Google Scholar
• Roozbeh M, Arashi M. Shrinkage ridge regression in partial linear models. Commun Stat Theory Methods. 2016;45(20):6022–6044.
   Web of Science ®Google Scholar
• Roozbeh M. Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterion. Comput Stat Data Anal. 2018;117:45–61.
   Web of Science ®Google Scholar
• Amini M, Roozbeh M. Improving the prediction performance of the LASSO by subtracting the additive structural noises. Comput Stat. 2019;34(1):415–432.
   Web of Science ®Google Scholar
• Brockwell PJ, Davis RA. Time series: theory and methods. Springer-Verlag: New York; 1991.
   Google Scholar
• Sheather SJ. A modern approach to regression with R. New York: Springer; 2009.
   Google Scholar
• Chen R, Liang H, Wang J. On determination of linear components in additive models. J Nonparametr Stat. 2011;23:367–383.
   Web of Science ®Google Scholar
• Zhang HH, Cheng G, Liu Y. Linear or nonlinear? Automatic structure discovery for partially linear models. J Am Stat Assoc. 2011;106:1099–1112.
   PubMed Web of Science ®Google Scholar
• Huang J, Wei F, Ma S. Semiparametric regression pursuit. Stat Sin. 2012;22:1403–1426.
   PubMed Web of Science ®Google Scholar
• Lian H. Variable selection in high-dimensional partly linear additive models. J Nonparametr Stat. 2012;24(4):825–839.
   Web of Science ®Google Scholar
• Lian H, Liang H, Ruppert D. Separation of covariates into nonparametric and parametric parts in high-dimensional partially linear additive models. Stat Sin. 2015;25:591–607.
   Web of Science ®Google Scholar
• Kazemi M, Shahsavani D, Arashi M. Variable selection and structure identification for ultrahigh-dimensional partially linear additive models with application to cardiomyopathy microarray data. Stat Optim Inf Comput. 2018;6(3):373–382.
   Google Scholar
• Kazemi M, Shahsavani D, Arashi M. A sure independence screening procedure for ultra-high dimensional partially linear additive models. J Appl Stat. 2019;46(8):1385–1403.
   Web of Science ®Google Scholar
• Gao JT. Nonlinear time series: semiparametric and nonparametric methods. London: Chapman & Hall/CRC; 2007.
   Google Scholar
• You JH, Chen G. Semiparametric generalized least squares estimation in partially linear regression models with correlated errors. J Stat Plan Inference. 2007;137:117–132.
   Web of Science ®Google Scholar
• Aneiros-pérez G, Vilar-Ferńandez JM. Local polynomial estimation in partial linear regression models under dependence. J Stat Plan Inference. 2008;52:2757–2777.
   Google Scholar
• Roozbeh M, Arashi M, Niroumand HA. Ridge regression methodology in partial linear models with correlated errors. J Stat Comput Simul. 2011;81(4):517–528.
   Web of Science ®Google Scholar
• Amini M, Roozbeh M. Optimal partial ridge estimation in restricted semiparametric regression models. J Multivar Anal. 2015;136:26–40.
   Web of Science ®Google Scholar
• Li D, Li G, You J. Significant variable selection and autoregressive order determination for time series partially linear models. J Time Ser Anal. 2014;35(5):478–490.
   Web of Science ®Google Scholar
• Zheng S, Li D. Semiparametric time series regression modeling with a diverging number of parameters. Stat Neerl. 2018;72(2):90–108.
   Web of Science ®Google Scholar
• Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc. 2001;96:1348–1360.
   Web of Science ®Google Scholar
• Box GEP, Jenkins GM, Reinsel GC. Time series analysis-forecasting and control. 3rd ed. New York: Prentice Hall; 1994.
   Google Scholar