Rethinking Biased Estimation: Improving Maximum Likelihood and the Cramér–Rao Bound
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چکیده
منابع مشابه
Rethinking Biased Estimation: Improving Maximum Likelihood and the Cramér-Rao Bound
One of the prime goals of statistical estimation theory is the development of performance bounds when estimating parameters of interest in a given model, as well as constructing estimators that achieve these limits. When the parameters to be estimated are deterministic, a popular approach is to bound the mean-squared error (MSE) achievable within the class of unbiased estimators. Although it is...
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An important aspect of estimation theory is characterizing the best achievable performance in a given estimation problem, as well as determining estimators that achieve the optimal performance. The traditional Cramér-Rao type bounds provide benchmarks on the variance of any estimator of a deterministic parameter vector under suitable regularity conditions, while requiring a-priori specification...
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ژورنال
عنوان ژورنال: Foundations and Trends® in Signal Processing
سال: 2007
ISSN: 1932-8346,1932-8354
DOI: 10.1561/2000000008