Name
Abstract | ||
|---|---|---|
In applications, the traditional estimation procedure generally begins with model selection. Once a specific model is selected,
subsequent estimation is conducted under the selected model without consideration of the uncertainty from the selection process.
This often leads to the underreporting of variability and too optimistic confidence sets. Model averaging estimation is an
alternative to this procedure, which incorporates model uncertainty into the estimation process. In recent years, there has
been a rising interest in model averaging from the frequentist perspective, and some important progresses have been made.
In this paper, the theory and methods on frequentist model averaging estimation are surveyed. Some future research topics
are also discussed. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11424-009-9198-y | J. Systems Science & Complexity |
Keywords | Field | DocType |
model selection,optimglity,optimality.,frequentist model averaging,asymptotic theory,adaptive regression | Econometrics,Mathematical optimization,Frequentist inference,Model selection,Mathematics | Journal |
Volume | Issue | ISSN |
22 | 4 | 1559-7067 |
Citations | PageRank | References |
1 | 0.43 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| haiying | 1 | 4 | 3.72 |
| wang | 2 | 28 | 3.86 |
| Guohua Zou | 3 | 12 | 5.72 |
| Guohua Zou | 4 | 12 | 5.72 |
| zhang | 5 | 1 | 0.43 |
| z u | 6 | 1 | 0.43 |