Estimation and outlier detection of random coefficient autoregressive models / Norli Anida binti Abdullah
The class of random coe±cient autoregressive (RCA) models has been con-sidered in many areas of science due to its rich applications. We review two methods of RCA parameter estimation, namely least squares and estimating functions. An iterative method based on the estimating functions is proposed t...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
2009
|
| Subjects: | |
| Online Access: | http://studentsrepo.um.edu.my/4353/1/NorliAnida_SGR070101_MSc_Statistics.pdf http://studentsrepo.um.edu.my/4353/1/NorliAnida_SGR070101_MSc_Statistics.pdf http://studentsrepo.um.edu.my/4353/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The class of random coe±cient autoregressive (RCA) models has been con-sidered in many areas of science due to its rich applications. We review two methods of RCA parameter estimation, namely least squares and estimating
functions. An iterative method based on the estimating functions is proposed to improve the existing RCA parameter estimation. This study is then fol-lowed by investigating the robustness of the three estimates when outliers exist
in the RCA process. Simulation studies are carried out to investigate the per-formance of parameter estimation and robustness of the estimates.Further, the outlier detection procedure for the RCA process is proposed.In this study, a procedure by Chang et al. (1988) has been extended to detect
additive and innovational outliers in the RCA process. A simulation study is carried out to investigate the performance of the procedures. It is found that, in general, these procedures work well in detecting outliers. Finally, we apply the suggested procedures to a real data set to show the importance of the study in practice. |
|---|