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Articles

Solution for a time-series AR model based on robust TLS estimation

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Pages 768-779 | Received 23 May 2018, Accepted 21 Oct 2018, Published online: 23 Jan 2019

References

  • Adcock RJ. 1877. Note on the method of least squares. Analyst. 4(6):183–184.
  • Ai-Sharadqah A, Ho KC. 2018. Constrained Cramér-Rao lower bound in errors-in-variables (EIV) models: revisited. Stat Prob Lett. 135(4):118–126.
  • Datteo A, Busca G, Quattromani G. 2018. On the use of AR models for SHM: a global sensitivity and uncertainty analysis framework. Reliab Eng Syst Safety. 170:99–115.
  • Di Giacinto V. 2006. A generalized space-time ARMA model with an application to regional unemployment analysis in Italy. Int Reg Sci Rev. 29(2):159–198.
  • do Nascimento Camelo H, Sergio Lucio P, Vercosa Leal Junior JB, de Carvalho PCM, dos Santos DVG. 2018. Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks. Energy. 151:347–357.
  • Fang X, Li B, Alkhatib H, Zeng W, Yao Y. 2017. Bayesian inference for the errors-in-variables model. Stud Geophys Geod. 61(1):35–52.
  • Golub GH, Van Loan CF. 1980. An analysis of the total least squares problem. SIAM J Num Anal. 17(6):883–893.
  • Hirata Y, Aihara K. 2017. Improving time series prediction of solar irradiance after sunrise: comparison among three methods for time series prediction. Sol Energy. 149:294–301.
  • Huber PJ. 1964. Robust estimation of a location parameter. Ann Math Stat. 35(1):73–101.
  • Kurt S, Batu Tunay K. 2015. STARMA models estimation with Kalman filter: the case of regional bank deposits. Procedia-Soc Behav Sci. 195:2537–2547.
  • Leyang W, Haiyan L, Yangmao W. 2017. Total least squares method inversion for coseismic slip distribution. Acta Geodaetica et Cartographica Sinica. 46(3):307–315.
  • Mahboub V. 2012. On weighted total least-squares for geodetic transformation. J Geod. 86(5):359–367.
  • Mahboub V. 2014. Variance component estimation in errors-in-variables models and a rigorous total least-squares approach. Stud Geophys Geod. 58(1):17–40.
  • Pfeifer PE, Deutsch SJ. 1980. A three-stage iterative procedure for space-time modeling. Technometrics. 22(1):35–47.
  • Pfeifer PE, Ve Bodily SE. 1990. A test of space-time ARMA modeling and forecasting with an application to real estate prices. J Forecast. 16:255–272.
  • Rousseeuw P, Wagner J. 1994. Robust regression with a distribution intercept using least median of squares. Comput Stat Data An. 17:65–76.
  • Schaffrin B, Wieser A. 2008. On weighted total least-squares adjustment for linear regression. J Geod. 82(7):415–421.
  • Schaffrin B, Uzun S. 2011. Error-in-variables for mobile mapping algorithms in the presence of outliers. ISPRS Archives. 22:377–387.
  • Shen Y, Li B, Chen Y. 2011. An iterative solution of weighted total least-squares adjustment. J Geod. 85(4):229–238.
  • Tuncel KS, Baydogan MG. 2018. Autoregressive forests for multivariate time series modeling. Pattern Recogn. 73:202–215.
  • Wan H, Xiao L. 2016. Variational Bayesian learning for robust AR modeling with the presence of sparse impulse noise. DSP. 59:108.
  • Wang B, Li J, Liu C. 2016. A robust weighted total least squares algorithm and its geodetic applications. Stud Geophys Geod. 60(2):177–194.
  • Yang Y. 1999. Robust estimation of geodetic datum transformation. J Geod. 73(5):268–274.
  • Zhangzhen S, Tianhe X. 2012. Prediction of earth rotation parameters based on improved weighted least squares and autoregressive model. Geod Geodyn. 3(3):57–64.