The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Stephan Matzka - One of the best experts on this subject based on the ideXlab platform.
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a confidence measure for vehicle tracking based on a generalization of Bayes estimation
IEEE Intelligent Vehicles Symposium, 2010Co-Authors: Richard Altendorfer, Stephan MatzkaAbstract:In safety-critical driver assistance systems such as automatic emergency braking that require the estimation of the vehicle's environment usually a measure of confidence or probability of existence for tracked objects is required. We review and assess existing approaches of obtaining such measures. We propose a new method of computing a probability of existence by relaxing the underlying assumption of a Bayes Estimator. The benefits of this approach compared to a standard Bayes Estimator are demonstrated and illustrated by experimental results.
Richard Altendorfer - One of the best experts on this subject based on the ideXlab platform.
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a confidence measure for vehicle tracking based on a generalization of Bayes estimation
IEEE Intelligent Vehicles Symposium, 2010Co-Authors: Richard Altendorfer, Stephan MatzkaAbstract:In safety-critical driver assistance systems such as automatic emergency braking that require the estimation of the vehicle's environment usually a measure of confidence or probability of existence for tracked objects is required. We review and assess existing approaches of obtaining such measures. We propose a new method of computing a probability of existence by relaxing the underlying assumption of a Bayes Estimator. The benefits of this approach compared to a standard Bayes Estimator are demonstrated and illustrated by experimental results.
Su-yun Huang - One of the best experts on this subject based on the ideXlab platform.
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Wavelet based empirical Bayes estimation for the uniform distribution
Statistics & Probability Letters, 1997Co-Authors: Su-yun HuangAbstract:The theory of wavelets is a fast developing component in mathematics with great potential in statistical applications. In this work, we incorporate the wavelet tool into the method of empirical Bayes estimation. Asymptotic behavior of the wavelet based empirical Bayes Estimator is investigated. The kernel based Estimator studied by Nogami (1988) has convergence rate O(n-1/2). We show that the wavelet based empirical Bayes Estimator attains the rate O(n-2s/(2s+1)), where s [greater-or-equal, slanted] 1 is the regularity index of the marginal pdf fG. Derivatives considered here are distributional derivatives.
Isaac Meilijson - One of the best experts on this subject based on the ideXlab platform.
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an example of an improvable rao blackwell improvement inefficient maximum likelihood Estimator and unbiased generalized Bayes Estimator
The American Statistician, 2016Co-Authors: Tal Galili, Isaac MeilijsonAbstract:The Rao–Blackwell theorem offers a procedure for converting a crude unbiased Estimator of a parameter θ into a “better” one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao–Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao–Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood Estimator is inefficient, and an unbiased generalized Bayes Estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated.[Received December 2014. Revised September 2015.]
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An Example of an Improvable Rao–Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator
The American statistician, 2016Co-Authors: Tal Galili, Isaac MeilijsonAbstract:The Rao-Blackwell theorem offers a procedure for converting a crude unbiased Estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood Estimator is inefficient, and an unbiased generalized Bayes Estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised September 2015.].
Mahmoud Afshari - One of the best experts on this subject based on the ideXlab platform.
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Wavelet shrinkage generalized Bayes estimation for elliptical distribution parameter’s under LINEX loss
International Journal of Wavelets Multiresolution and Information Processing, 2019Co-Authors: Hamid Karamikabir, Mahmoud AfshariAbstract:In this paper, the generalized Bayes Estimator of elliptical distribution parameter’s under asymmetric Linex error loss function is considered. The new shrinkage generalized Bayes Estimator by appl...
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wavelet shrinkage generalized Bayes estimation for elliptical distribution parameter s under linex loss
International Journal of Wavelets Multiresolution and Information Processing, 2019Co-Authors: Hamid Karamikabir, Mahmoud AfshariAbstract:In this paper, the generalized Bayes Estimator of elliptical distribution parameter’s under asymmetric Linex error loss function is considered. The new shrinkage generalized Bayes Estimator by appl...
