Strongly Consistent Model Order Selection for Estimating 2-D Sinusoids in Colored Noise
| dc.creator | Kliger, Mark | |
| dc.creator | Francos, Joseph M. | |
| dc.date | 2008-01-18 | |
| dc.date.accessioned | 2026-07-07T08:55:15Z | |
| dc.date.available | 2026-07-07T08:55:15Z | |
| dc.description | We consider the problem of jointly estimating the number as well as the parameters of two-dimensional sinusoidal signals, observed in the presence of an additive colored noise field. We begin by elaborating on the least squares estimation of 2-D sinusoidal signals, when the assumed number of sinusoids is incorrect. In the case where the number of sinusoidal signals is under-estimated we show the almost sure convergence of the least squares estimates to the parameters of the dominant sinusoids. In the case where this number is over-estimated, the estimated parameter vector obtained by the least squares estimator contains a sub-vector that converges almost surely to the correct parameters of the sinusoids. Based on these results, we prove the strong consistency of a new model order selection rule. | |
| dc.identifier | https://arxiv.org/abs/0801.2790 | |
| dc.identifier | http://arxiv.org/abs/0801.2790 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/146215 | |
| dc.subject | Methodology | |
| dc.subject | Statistics Theory | |
| dc.title | Strongly Consistent Model Order Selection for Estimating 2-D Sinusoids in Colored Noise | |
| dc.type | text |