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Autoregressive Lag — Order Selection Using Conditional Saddlepoint Approximations
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2017 |
| Abstract | A new method for determining the lag order of the autoregressive polynomial in regression models with autocorrelated normal disturbances is proposed. It is based on a sequential testing procedure using conditional saddlepoint approximations and permits the desire for parsimony to be explicitly incorporated, unlike penalty-based model selection methods. Extensive simulation results indicate that the new method is usually competitive with, and often better than, common model selection methods. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://mdpi.com/2225-1146/5/3/43/pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation Autocorrelation Autoregressive model Genetic Selection License Maximum parsimony (phylogenetics) Model selection Occam's razor Polynomial STMN1 gene Simulation |
| Content Type | Text |
| Resource Type | Article |
