Name
Abstract | ||
|---|---|---|
When a dataset is corrupted by noise, the model for data generating process is misspecified and can cause parameter-estimation problems. For example, in the case of a Gaussian autoregressive (AR) process corrupted by noise, data are more accurately modeled as an AR-moving-average process rather than an AR process. This misspecification leads to bias, and hence, low resolution in AR spectral estima... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSP.2018.2890363 | IEEE Transactions on Signal Processing |
Keywords | DocType | Volume |
Maximum likelihood estimation,Autoregressive processes,Mathematical model,White noise,Random processes,Data models | Journal | 67 |
Issue | ISSN | Citations |
8 | 1053-587X | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |