Point and interval estimation method for auto-regressive model with nonnormal error

Bo Mi Lim, Jongwoo Kim, Sung Shick Kim, Jun-Geol Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Estimation in time series analysis aids in making a reasonable decision by providing a value for point estimation and a range of interval estimation. An auto-regressive model is designed for the time series analysis. However, the auto-regressive model may cause decreasing accuracy and prediction in estimating parameters because it uses the assumption that the distribution of error term follows a normal distribution. In reality, there are plenty of data indicating that the distribution of error term does not follow the normal distribution. Thus, we propose a method for solving this problem by using a Pearson distribution system and maximum likelihood estimation. Compared with existing methods, the proposed method can be applied to various time series data requiring high accuracy and prediction.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Congress on Big Data, BigData 2013
Pages379-386
Number of pages8
DOIs
Publication statusPublished - 2013 Oct 28
Event2013 IEEE International Congress on Big Data, BigData 2013 - Santa Clara, CA, United States
Duration: 2013 Jun 272013 Jul 2

Other

Other2013 IEEE International Congress on Big Data, BigData 2013
CountryUnited States
CitySanta Clara, CA
Period13/6/2713/7/2

Keywords

  • Auto-Regressive Model
  • Interval Estimation
  • Maximum Likelihood Estimation
  • Nonnormal data
  • Pearson Distribution System
  • Point Estimation

ASJC Scopus subject areas

  • Computer Science Applications

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  • Cite this

    Lim, B. M., Kim, J., Kim, S. S., & Baek, J-G. (2013). Point and interval estimation method for auto-regressive model with nonnormal error. In Proceedings - 2013 IEEE International Congress on Big Data, BigData 2013 (pp. 379-386). [6597161] https://doi.org/10.1109/BigData.Congress.2013.57