Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지
DOI QR코드

DOI QR Code

Support vector quantile regression for autoregressive data

  • Received : 2014.09.15
  • Accepted : 2014.10.16
  • Published : 2014.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper we apply the autoregressive process to the nonlinear quantile regression in order to infer nonlinear quantile regression models for the autocorrelated data. We propose a kernel method for the autoregressive data which estimates the nonlinear quantile regression function by kernel machines. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of quantile regression function in the presence of autocorrelation between data.

Keywords

References

  1. Hwang, C. (2010). M-quantile regression using kernel machine technique. Journal of the Korean Data & Information Science Society, 21, 973-981.
  2. Juditsky, A., Hjalmarsson, H., Benveniste, A., Deylon, B., Ljung, L., Sjoberg, J. and Zhang, Q. (1995). Nonlinear black-box modelling in system identification : Mathematical foundations. Automatica, 31, 1725-1750. https://doi.org/10.1016/0005-1098(95)00119-1
  3. Kim, M. S., Park, H., Hwang, C. and Shim, J. (2013). GACV for claims reserving via kernel machine. Journal of the Korean Data & Information Science Society, 19, 1419-1427.
  4. Koenker, R. (2005). Quantile regression, Cambridge University Press, Cambridge.
  5. Koenker, R. and Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 40, 122-142.
  6. Koenker, R. and Bassett. G. (1978). Regression quantile. Econometrica, 46, 33-50. https://doi.org/10.2307/1913643
  7. Li, Y., Liu, Y. and Zhu, J. (2007). Quantile regression in reproducing kernel Hilbert spaces. Journal of the American Statistical Association, 102, 255-268. https://doi.org/10.1198/016214506000000979
  8. Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society A, 415-446.
  9. Shim, J and Seok, K. H. (2013). GACV for partially linear SVR. Journal of the Korean Data & Information Science Society, 24, 391-399. https://doi.org/10.7465/jkdi.2013.24.2.391
  10. Yu, K., Lu, Z. and Stander, J. (2003). Quantile regression: Applications and current research area. The Statistician, 52, 331-350.
  11. Yuan, M. (2006). GACV for quantile smoothing splines. Computational Statistics & Data Analysis, 50, 813-829. https://doi.org/10.1016/j.csda.2004.10.008

Cited by

  1. Remarks on correlated error tests vol.27, pp.2, 2016, https://doi.org/10.7465/jkdi.2016.27.2.559
  2. Deep LS-SVM for regression vol.27, pp.3, 2016, https://doi.org/10.7465/jkdi.2016.27.3.827
  3. Geographically weighted kernel logistic regression for small area proportion estimation vol.27, pp.2, 2016, https://doi.org/10.7465/jkdi.2016.27.2.531
  4. Multioutput LS-SVR based residual MCUSUM control chart for autocorrelated process vol.27, pp.2, 2016, https://doi.org/10.7465/jkdi.2016.27.2.523
  5. 국제곡물가격에 대한 기후의 고차 선형 적률 인과관계 연구 vol.28, pp.1, 2017, https://doi.org/10.7465/jkdi.2017.28.1.67
  6. 원유가격에 대한 환율의 인과관계 : 비모수 분위수검정 접근 vol.28, pp.2, 2017, https://doi.org/10.7465/jkdi.2017.28.2.361