Name
Abstract | ||
|---|---|---|
As the Information Theoretic Criteria (ITC) for AR order selection are derived under the strong hypothesis of stationarity of the mea- sured signals, it is not straightforward to utilize them in conjunction with the forgetting factor least-squares algorithms. In the previ- ous literature, the attempts for solving the problem were focused on the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC) and the Predictive Least Squares (PLS). This study provides a variant of the Predictive Densities Criterion (PDC) that it is compatible with the forgetting factor least-squares algorithms. We also introduce a modified version of the very new Sequentially Normalized Maximum Likelihood (SNML) criterion. Additionally, we give rigorous proofs for results concerning PLS and SNML. |
Year | Venue | Keywords |
|---|---|---|
2008 | Lausanne | least square,akaike information criterion,bayesian information criterion |
Field | DocType | ISSN |
Least squares,Bayesian information criterion,Akaike information criterion,Stepwise regression,Pattern recognition,Determining the number of clusters in a data set,Algorithm,Normalized maximum likelihood,Mathematical proof,Artificial intelligence,Mathematics,Estimator | Conference | 2219-5491 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ciprian Doru Giurcaneanu | 1 | 8 | 3.84 |
| Seyed Alireza Razavi | 2 | 42 | 7.77 |