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An improved Bayes empirical Bayes estimator [PDF]
International Journal of Mathematics and Mathematical Sciences, 2003 Consider an experiment yielding an observable random quantity X whose distribution Fθ depends on a parameter θ with θ being distributed according to some distribution G0. We study the Bayesian estimation problem of θ under squared error loss function based on X, as well as some additional data available from other similar experiments according to an ...
R. J. Karunamuni, N. G. N. Prasad
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Robust Bayes-like estimation: Rho-Bayes estimation [PDF]
The Annals of Statistics, 2020 68 ...
Baraud, Yannick, Birgé, Lucien
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Bayes Estimators for Phylogenetic Reconstruction [PDF]
Systematic Biology, 2011 Tree reconstruction methods are often judged by their accuracy, measured by how close they get to the true tree. Yet most reconstruction methods like ML do not explicitly maximize this accuracy. To address this problem, we propose a Bayesian solution.
Peter Huggins +5 more
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The Annals of Mathematical Statistics, 1967 Throughout this paper we are concerned with the problem of estimating a real parameter when the loss function is such that the Bayes estimate exists, is unique, and satisfies a simple Equation, (1.5). If the estimate is unbiased (in the general sense of Lehmann [3]) we show under weak conditions that it must satisfy another Equation, (1.14).
Bickel, Peter J., Blackwell, David
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On the Consistency of Bayes Estimates
The Annals of Statistics, 1986 The authors of this special invited paper give the following summary: ''We discuss frequency properties of Bayes rules, paying special attention to consistency. Some new and fairly natural counterexamples are given, involving nonparametric estimates of location.
Diaconis, Persi, Freedman, David
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Interval Estimation Naïve Bayes [PDF]
, 2003 Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier called naïve Bayes is competitive with state of the art classifiers. This simple approach stands from assumptions of conditional independence among features given the class.
Robles Forcada, Víctor +4 more
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Empirical Bayes and Full Bayes for Signal Estimation
CoRR, 2014 We consider signals that follow a parametric distribution where the parameter values are unknown. To estimate such signals from noisy measurements in scalar channels, we study the empirical performance of an empirical Bayes (EB) approach and a full Bayes (FB) approach.
Yanting Ma +3 more
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Maximum a posteriori estimators as a limit of Bayes estimators [PDF]
Mathematical Programming, 2018 Maximum a posteriori and Bayes estimators are two common methods of point estimation in Bayesian Statistics. It is commonly accepted that maximum a posteriori estimators are a limiting case of Bayes estimators with 0-1 loss. In this paper, we provide a counterexample which shows that in general this claim is false.
Robert L. Bassett, Julio Deride
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Nonparametric Bayes-risk estimation [PDF]
IEEE Transactions on Information Theory, 1971 Two nonparametric methods to estimate the Bayes risk using classified sample sets are described and compared. The first method uses the nearest neighbor error rate as an estimate to bound the Bayes risk. The second method estimates the Bayes decision regions by applying Parzen probability-density function estimates and counts errors made using these ...
Stanley C. Fralick, Richard W. Scott
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BAYES RISKS OF ESTIMATORS OF ESTIMABLE PARAMETERS [PDF]
Bulletin of Mathematical Statistics, 1980 For the estimable parameter of degree 2, throughout this paper, we consider 02 with h2 such that h2(x, x) and h2(x, x)=0 for any x, yEX. As estimators of estimable parameters, U-statistics and differentiable statistical functions are well known. (See, for example, Hoeffding (1948) and von Mises (1947).) For an estimable parameter of degree 1, the U ...
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