Name
Abstract | ||
|---|---|---|
The first-order Poisson autoregressive model may be suitable in situations where the time series data are non-negative integer valued. In this article, we propose a new parameter estimator based on empirical likelihood. Our results show that it can lead to efficient estimators by making effective use of auxiliary information. As a by-product, a test statistic is given, testing the randomness of the parameter. The simulation values show that the proposed test statistic works well. We have applied the suggested method to a real count series. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00180-021-01157-5 | Computational Statistics |
Keywords | DocType | Volume |
Integer-valued time series, Empirical likelihood, Test statistic | Journal | 37 |
Issue | ISSN | Citations |
3 | 0943-4062 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feilong Lu | 1 | 0 | 0.34 |
| yang | 2 | 15 | 7.73 |