FINITE SAMPLE CRITERIA FOR AUTOREGRESSIVE MODEL ORDER SELECTION

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KARIMI M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION B- ENGINEERING Year:2007 | Volume:31 | Issue:B3 Page(s): 329-344

Download Full-Text

Persian Verion

View:

659

Download:

184

Cites:

Information Journal Paper

Title

FINITE SAMPLE CRITERIA FOR AUTOREGRESSIVE MODEL ORDER SELECTION

Author(s)

KARIMI M. | Issue Writer Certificate

Keywords

AUTOREGRESSIVE PROCESSQ3

AR MODELQ3

ORDER SELECTIONQ3

INFORMATION CRITERIONQ3

RESIDUAL VARIANCEQ3

PREDICTION ERRORQ3

Abstract

The existing theoretically derived ORDER SELECTION criteria for autoregressive (AR) processes have poor performance in the finite sample case. In this paper, the least-squares-forward (LSF) is considered as the AR parameter estimation method, and new theoretical approximations are derived for the expectations of RESIDUAL VARIANCE and PREDICTION ERROR. These approximations are especially useful in the finite sample case and are derived for AR processes with arbitrary statistical distributions. New ORDER SELECTION criteria for AR processes are derived using these approximations. In a simulation study, the performance of the proposed criteria relative to other criteria is examined in the finite sample case. Simulation results show that the performance of the proposed criteria is much better than the other theoretically derived criteria.

Cites

No record.

References

No record.

Cite

APA: Copy

KARIMI, M.. (2007). FINITE SAMPLE CRITERIA FOR AUTOREGRESSIVE MODEL ORDER SELECTION. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION B- ENGINEERING, 31(B3), 329-344. SID. https://sid.ir/paper/62347/en

Vancouver: Copy

KARIMI M.. FINITE SAMPLE CRITERIA FOR AUTOREGRESSIVE MODEL ORDER SELECTION. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION B- ENGINEERING[Internet]. 2007;31(B3):329-344. Available from: https://sid.ir/paper/62347/en

IEEE: Copy

M. KARIMI, “FINITE SAMPLE CRITERIA FOR AUTOREGRESSIVE MODEL ORDER SELECTION,” IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION B- ENGINEERING, vol. 31, no. B3, pp. 329–344, 2007, [Online]. Available: https://sid.ir/paper/62347/en

Related Journal Papers