Name
Title | ||
|---|---|---|
Optimally Distinguishable Distributions: a New Approach to Composite Hypothesis Testing With Applications to the Classical Linear Model |
Abstract | ||
|---|---|---|
The newest approach to composite hypothesis testing proposed by Rissanen relies on the concept of optimally distinguishable distributions (ODD). The method is promising, but so far it has only been applied to a few simple examples. We derive the ODD detector for the classical linear model. In this framework, we provide answers to the following problems that have not been previously investigated in the literature: i) the relationship between ODD and the widely used Generalized Likelihood Ratio Test (GLRT); ii) the connection between ODD and the information theoretic criteria applied in model selection. We point out the strengths and the weaknesses of the ODD method in detecting subspace signals in broadband noise. Effects of the subspace interference are also evaluated. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSP.2009.2017568 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
information theory,maximum likelihood estimation,classical linear model,composite hypothesis testing,generalized likelihood ratio test,information theoretic criteria,minimum description length,optimally distinguishable distributions,Generalized likelihood ratio test,information theoretic criteria,linear model,minimum description length,optimally distinguishable distributions | Information theory,Combinatorics,Mathematical optimization,Likelihood-ratio test,Detection theory,Subspace topology,Linear model,Minimum description length,Model selection,Algorithm,Statistical hypothesis testing,Mathematics | Journal |
Volume | Issue | ISSN |
57 | 7 | 1053-587X |
Citations | PageRank | References |
2 | 0.44 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seyed Alireza Razavi | 1 | 42 | 7.77 |
| Ciprian Doru Giurcaneanu | 2 | 8 | 3.84 |