Table of Contents Author Guidelines Submit a Manuscript
Journal of Probability and Statistics
Volume 2011 (2011), Article ID 372512, 14 pages
http://dx.doi.org/10.1155/2011/372512
Research Article

Joint Estimation Using Quadratic Estimating Function

1Department of Statistics, University of Manitoba, 338 Machray Hall, Winnipeg, MB, Canada R3T 2N2
2University of Waterloo, Waterloo, ON, Canada N2L 2G1

Received 12 January 2011; Revised 10 March 2011; Accepted 11 April 2011

Academic Editor: Ricardas Zitikis

Copyright © 2011 Y. Liang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. V. P. Godambe, “The foundations of finite sample estimation in stochastic processes,” Biometrika, vol. 72, no. 2, pp. 419–428, 1985. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  2. A. Thavaneswaran and B. Abraham, “Estimation for nonlinear time series models using estimating equations,” Journal of Time Series Analysis, vol. 9, no. 1, pp. 99–108, 1988. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  3. U. V. Naik-Nimbalkar and M. B. Rajarshi, “Filtering and smoothing via estimating functions,” Journal of the American Statistical Association, vol. 90, no. 429, pp. 301–306, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  4. A. Thavaneswaran and C. C. Heyde, “Prediction via estimating functions,” Journal of Statistical Planning and Inference, vol. 77, no. 1, pp. 89–101, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  5. S. A. Chandra and M. Taniguchi, “Estimating functions for nonlinear time series models. Nonlinear non-Gaussian models and related filtering methods,” Annals of the Institute of Statistical Mathematics, vol. 53, no. 1, pp. 125–141, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  6. T. Merkouris, “Transform martingale estimating functions,” The Annals of Statistics, vol. 35, no. 5, pp. 1975–2000, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  7. M. Ghahramani and A. Thavaneswaran, “Combining estimating functions for volatility,” Journal of Statistical Planning and Inference, vol. 139, no. 4, pp. 1449–1461, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  8. B. G. Lindsay, “Using empirical partially Bayes inference for increased efficiency,” The Annals of Statistics, vol. 13, no. 3, pp. 914–931, 1985. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  9. M. Crowder, “On linear and quadratic estimating functions,” Biometrika, vol. 74, no. 3, pp. 591–597, 1987. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  10. R. F. Engle and J. R. Russell, “Autoregressive conditional duration: a new model for irregularly spaced transaction data,” Econometrica, vol. 66, no. 5, pp. 1127–1162, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  11. D. F. Nicholls and B. G. Quinn, “The estimation of random coefficient autoregressive models. I,” Journal of Time Series Analysis, vol. 1, no. 1, pp. 37–46, 1980. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  12. D. Tjøstheim, “Some doubly stochastic time series models,” Journal of Time Series Analysis, vol. 7, no. 1, pp. 51–72, 1986. View at Publisher · View at Google Scholar
  13. A. N. Shiryayev, Probability, vol. 95 of Graduate Texts in Mathematics, Springer, New York, NY, USA, 1984.