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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pham, T. deFigueiredo, R. |
| Copyright Year | 1987 |
| Description | Author affiliation: Rice University, Houston, Texas (Pham, T.) |
| Abstract | In this paper, we investigate in detail the properties of the maximum likelihood estimator of the generalized p-Gaussian (gpG) probability density function (pdf) from N independent identically distributed (iid) samples, especially in the context of the deconvolution problem under gpG white noise. In the first part, we describe the properties of the estimator independently of the application. In the second part, we obtain the solution of the above-mentioned deconvolution problem as the solution of a minimum norm problem in an lpnormed space. In the present paper, we show that such a minimum norm solution is the maximum likelihood estimate of the system function parameters and show that such an estimate is unbiased, with the lower bound of the variance of the error equal to the Cramer-Rao lower bound, and the upper bound derived from the concept of a generalized inverse, both of which we also give. |
| Starting Page | 49 |
| Ending Page | 52 |
| File Size | 128032 |
| Page Count | 4 |
| File Format | |
| DOI | 10.1109/ICASSP.1987.1169626 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1987-04-06 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Maximum likelihood estimation Deconvolution Maximum likelihood detection Additive noise Earth Application software White noise Upper bound Reflectivity Linear programming |
| Content Type | Text |
| Resource Type | Article |
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