Results 221 to 230 of about 26,781 (264)

Biased Estimators and Unstable Betas

The Journal of Finance, 1980
STUDIES [1, 2, 3, 5, 8, 10] ESTIMATING AND ANALYZING the stability of security betas have theoretical and practical implications. The measurement of pure, or systematic, risk of securities is important in portfolio selection and in applications of the Capital Asset Pricing Model (CAPM).
Scott, Elton, Brown, Stewart
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Biased Parameter Estimation in LDA

2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008
Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization, manifold discovery and other application in discrete data. However, LDA alone does not perform well in IR application. We alleviate it by biased parameter estimation, which makes the topics in LDA more independent than standard LDA.
Boqiu Yuan, Yiming Zhou, Lin Li
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State estimation with biased observations

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1999
State estimation is difficult when the system has multiple modes of operation. Modal transitions create discontinuities in the reference point for the local state variables. The uncertain reference point increases the ambiguity in the state measurement. The paper presents an estimation algorithm that can be used in multimodal applications.
David D. Sworder, John E. Boyd
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Performance characteristics of biased estimators

ICASSP '84. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
The performance characteristics of biased coherence estimators are developed and displayed as a function of the signal and processor parameters. Examples of biased coherence estimators include the magnitude-squared coherence (MSC) estimator and the normalized correlation envelope (NCE) estimator.
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Biased estimates of nonrenal clearance

Journal of Pharmaceutical Sciences, 2001
The goal of the investigation was to critically evaluate published values for oral nonrenal clearance and their postulated dependence on renal function with drugs administered orally to subjects with varying renal function. Derivation of the pertinent equations indicated that the values reported for oral nonrenal clearance tend to systematically ...
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A volumetric approach to biased estimation: Demonstration on shrinkage estimators

2016 IEEE International Conference on Digital Signal Processing (DSP), 2016
This work proposes a new approach, named as the volumetric design (VD), of developing biased estimators of deterministic parameters that are known in advance to belong to a compact subset in the parameter space. For analytical tractability, this approach is demonstrated on the choice of the shrinkage parameter of an estimator that scales the celebrated
Bikcora, C., Weiland, S.
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Stochastic kriging with biased sample estimates

ACM Transactions on Modeling and Computer Simulation, 2014
Stochastic kriging has been studied as an effective metamodeling technique for approximating response surfaces in the context of stochastic simulation. In a simulation experiment, an analyst typically needs to estimate relevant metamodel parameters and further do prediction; therefore, the impact of parameter estimation on the performance of the ...
Xi Chen 0040, Kyoung-Kuk Kim
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Rethinking biased estimation [Lecture Notes]

IEEE Signal Processing Magazine, 2008
In this lecture note we discuss methods to improve the accuracy of unbiased estimators used in many signal processing problems. Our approach is based on introducing a bias as a means of reducing the mean-squared error (MSE). The important aspect of our framework is that the reduction in MSE is guaranteed for all values of the unknown parameter.
Steven Kay, Yonina C. Eldar
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Batching on Biased Estimators

2022 Winter Simulation Conference (WSC), 2022
Shengyi He, Henry Lam
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A biased estimate of proportion

International Journal of Mathematical Education in Science and Technology, 1998
A simple method of finding a biased estimate of the population proportion is introduced. This biased estimate is derived by finding a confidence interval for the population proportion without using the concept of convergence in probability. While all existing biased estimates of the population proportion are based on specific prior distributions, the ...
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