Name
Abstract | ||
|---|---|---|
In parameter estimation, it is not a good choice to select a "best model" by some criterion when there is model uncertainty. Model averaging is commonly used under this circumstance. In this paper, transformation-based model averaged tail area is proposed to construct confidence interval, which is an extension of model averaged tail area method in the literature. The transformation-based model averaged tail area method can be used for general parametric models and even non-parametric models. Also, it asymptotically has a simple formula when a certain transformation function is applied. Simulation studies are carried out to examine the performance of our method and compare with existing methods. A real data set is also analyzed to illustrate the methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s00180-014-0514-1 | Computational Statistics |
Keywords | DocType | Volume |
confidence interval,model averaging,transformation-based model averaged tail area,Confidence interval,Model averaging,Transformation-based model averaged tail area | Journal | 29 |
Issue | ISSN | Citations |
6 | 0943-4062 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Yu | 1 | 0 | 0.34 |
| Wangli Xu | 2 | 9 | 6.40 |
| Lixing Zhu | 3 | 116 | 34.41 |