Results 281 to 290 of about 479,757 (331)
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Prediction with shrinkage estimators
Statistics, 1978It is demonstrated that the prediction mean square error for a general prediction design matrix may be reduced by using one of a general class of shrinkage estimators instead of the least squares estimator.Further, a general characterization is given of those situations in which the potential reduction in prediction mean square error is large.
P J Brown
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Partial Kelly portfolios and shrinkage estimators
2012 IEEE International Symposium on Information Theory Proceedings, 2012The log-optimal or Kelly portfolio forms the basis of a theoretically appealing investment strategy. However, it is difficult to compute, and this hinders its adoption in practice. In this paper we consider an approximate Kelly portfolio based on maximizing the expected value of a quadratic approximation to log utility.
Justin K. Rising, Abraham J. Wyner
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Communications in statistics. Simulation and computation, 2022
In this paper, the Bayesian and Bayesian shrinkage estimators of the scale parameter of two-parameter exponential distribution are provided based on the square error and Al-Bayyati loss functions and right censoring.
Seyed Rasuol Hosseini +2 more
semanticscholar +1 more source
In this paper, the Bayesian and Bayesian shrinkage estimators of the scale parameter of two-parameter exponential distribution are provided based on the square error and Al-Bayyati loss functions and right censoring.
Seyed Rasuol Hosseini +2 more
semanticscholar +1 more source
Penalized and ridge-type shrinkage estimators in Poisson regression model
Communications in statistics. Simulation and computation, 2020The paper considers the problem of estimation of the regression coefficients in a Poisson regression model under multicollinearity situation. We propose non-penalty Stein-type shrinkage ridge estimation approach when it is conjectured that some prior ...
M. N. Asl, H. Bevrani, R. A. Belaghi
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Robust shrinkage M-estimators of large covariance matrices
, 2016 :Robust high dimensional covariance estimators are considered, comprising regularized (linear shrinkage) modifications of Maronna's classical M-estimators.
Nicolas Auguin +2 more
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Effective Memory Shrinkage in Estimation
2018 IEEE International Symposium on Information Theory (ISIT), 2018It is known that a processor with limited memory consisting of an m-state machine can distinguish two coins with biases that differ by $1/m$ . On the other hand, the best additive accuracy with which the same processor can estimate the bias of a coin is only $1/\sqrt{m}$ .
Ayush Jain 0001, Himanshu Tyagi
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Stein‐type shrinkage estimators in gamma regression model with application to prostate cancer data
Statistics in Medicine, 2019Gamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control.
S. Mandal +3 more
semanticscholar +1 more source
Statistical estimation for hyper shrinkage
Digital Signal Processing, 2007A new shrinkage technique in wavelet domain called hyper shrinkage that uses hyperbolic function for improved denoising is explained. The methodology is statistically significant in terms of signal recovery and improving signal-to-noise ratio over both hard and soft shrinkage. A mathematical treatment of proposed shrinkage function shows an improvement
S. Poornachandra, Natesan Kumaravel
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A Shrinkage Estimator for Combination of Bioassays
Acta Mathematicae Applicatae Sinica, English Series, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiong, Jian, Chen, D. G., Yang, Zhen-Hai
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A volumetric approach to biased estimation: Demonstration on shrinkage estimators
2016 IEEE International Conference on Digital Signal Processing (DSP), 2016This 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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