332
Views
9
CrossRef citations to date
0
Altmetric
Original Articles

Application of shrinkage estimation in linear regression models with autoregressive errors

, &
Pages 3335-3351 | Received 10 Oct 2013, Accepted 26 Sep 2014, Published online: 21 Oct 2014

References

  • Fallahpour S, Ahmed SE, Doksum KA. L1 penalty and shrinkage estimation in partially linear models with random coefficient autoregressive errors. Appl Stoch Models Bus Ind. 2012;28:236–250.
  • Liang H, Song W. Improved estimation in multiple linear regression models with measurement error and general constraint. J Multivariate Anal. 2009;100:726–741.
  • Ahmed SE, Hossain S, Doksum KA. LASSO and shrinkage estimation in Weibull censored regression models. J Statist Plan Inference. 2012;142:1273-–1284.
  • Ohtani K, Wan ATK. Comparison of the stein and the useful estimators for the regression error variance under the Pitman nearness criterion when variables are omitted. Statist Pap. 2008;50:151–160.
  • Saleh AKME. Theory of preliminary test and stein-type estimation with applications. New York: Wiley; 2006.
  • Judge GG, Mittelhammer RC. A semiparametric basis for combining estimation problems under quadratic loss. J Am Statist Assoc. 2004;99:479–487.
  • Tibshirani R. Regression shrinkage and selection via the LASSO. J R Statist Soc: Ser B. 1996;58:267–288.
  • Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Statist Assoc. 2001;96:1348–1360.
  • Zou H. The adaptive LASSO. J Am Statist Assoc. 2006;101:1418–1429.
  • Wang H, Li G, Tsai C. Regression coefficient and autoregressive order shrinkage and selection via the LASSO. J R Statist Soc: Ser B. 2007;69:63–78.
  • Yoon YJ, Park C, Lee T. Penalized regression models with autoregressive error terms. J Statist Comput Simul. 2012:1–17. doi:10.1080/00949655.2012.669383.
  • Gupta S. A study of the asymptotic properties of LASSO estimates for correlated data [Electronic theses, treatises and dissertations], Paper-3896, 2009. Available from: http://diginole.lib.fsu.edu/etd/3896
  • Bunea F, Gupta S. A study of the asymptotic properties of Lasso for correlated data, Technical Report, Florida State University; 2010. Available from: http://stat.fsu.edu/techreports/M995.pdf
  • Fan J, Lv J. A selective overview of variable selection in high dimensional feature space. Statist Sin. 2010;20: 101–148.
  • Tibshirani R. Regression shrinkage and selection via the LASSO: a retrospective. J R Statist Soc: Ser B. 2011;73:273–282.
  • Tran MN. The loss rank criterion for variable selection in linear regression analysis. Scand J Stat. 2011;38:466–479.
  • Huang J, Ma S, Zhang C. Adaptive LASSO for sparse high-dimensional regression models. Statist Sin. 2008;18:1603–1618.
  • Kim Y, Choi H, Oh HS. Smoothly clipped absolute deviation on high dimensions. J Am Statist Assoc. 2008;103:1665–1673.
  • Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Statist Soc: Ser B. 2006;68:49–68.
  • Leng C, Lin Y, Wahba G. A note on the LASSO and related procedures in model selection. Statist Sin. 2006;16:1273–1284.
  • Tibshirani R, Saunders M, Rosset S, Zhu J, Knight K. Sparsity and smoothness via the fused LASSO. J R Statist Soc: Ser B. 2005;67:91–108.
  • Box GEP, Jenkins M, Reinsel GC. Time series analysis: forecasting and control. 4th ed. Englewood Cliffs, NJ: Prentice-Hall; 2008.
  • R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013.
  • Ulbricht J. Variable selection in generalized linear models [Unpublished Ph.D. thesis]. Munich: Ludwig Maximilians University; 2010.
  • Shumway RH, Azari AS, Pawitan Y. Modeling mortality fluctuations in Los Angeles as functions of pollution and weather effects. Environ Res. 1988;45:224–241.
  • Stoffer D. astsa: applied statistical time series analysis. R package version 1.1. 2012.
  • Shumway RH. Applied statistical time series analysis. Englewood Cliffs, NJ: Prentice-Hall; 1988.
  • Shumway RH, Stoffer DS. Time series analysis and its applications: with R examples. New York: Springer; 2006.
  • Basu R. High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008. Environ Health. 2009;8(40):1–13.
  • Armstrong B. Models for the relationship between ambient temperature and daily mortality. Epidemiology. 2006;17:624–631.
  • Peng RD, Dominici F. Statistical methods for environmental epidemiology with R: a case study in air pollution and health. New York: Springer; 2008.
  • Lawless JF, Singhal K. Efficient screening of non-normal regression models. Biometrics. 1978;34:318–327.
  • Judge GG, Bock ME. The statistical implication of pre-test and stein-rule estimators in econometrics. Amsterdam: North-Holland; 1978.

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.