Name
Abstract | ||
|---|---|---|
In estimating the parameter θ from a parametrized signal problem (with 0⩽θ⩽L) observed through Gaussian white noise, four useful and computable lower bounds for the Bayes risk are developed. For problems with different L and different signal to noise ratios, some bounds are superior to others. The lower bound obtained from taking the maximum of the four, serves not only as a good lower bound for the Bayes risk but also as a good lower bound for the minimax risks. Threshold behavior of the Bayes risk is also evident, as is shown in the lower bound |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1109/18.243453 | Information Theory, IEEE Transactions |
Keywords | Field | DocType |
Bayes methods,information theory,minimax techniques,parameter estimation,signal processing,Bayes risk,Gaussian white noise,lower bounds,minimax risk,signal parameter estimation,threshold behaviour | Information theory,Discrete mathematics,Minimax,Upper and lower bounds,Signal-to-noise ratio,White noise,Estimation theory,Gaussian noise,Mathematics,Bayes' theorem | Journal |
Volume | Issue | ISSN |
39 | 4 | 0018-9448 |
Citations | PageRank | References |
3 | 0.41 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brown, L.D. | 1 | 3 | 0.41 |
| Liu, R.C. | 2 | 3 | 0.41 |