Results 261 to 270 of about 7,690 (301)
iPRSue: Unbiased individual-level uncertainty estimation in polygenic risk scores
Jayasinghe D +4 more
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SSRN Electronic Journal, 2010 This paper considers estimation of an unknown distribution parameter in situations where we believe that the parameter belongs to a finite interval. We propose for such situations an interval shrinkage approach which combines in a coherent way an unbiased conventional estimator and non-sample information about the range of plausible parameter values ...
Vasyl Golosnoy, Roman Liesenfeld
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Shrinkage covariance matrix estimator applied to STAP detection
, 2014 International audienceIn the context of robust covariance matrix estimation, this work generalizes the shrinkage covariance matrix estimator introduced in [1, 2].
Frédéric Pascal
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Generalized Robust Shrinkage Estimator and Its Application to STAP Detection Problem
IEEE Transactions on Signal Processing, 2014 International audienceRecently, in the context of covariance matrix estimation, in order to improve as well as to regularize the performance of the Tyler's estimator [1] also called the Fixed-Point Estimator (FPE) [2], a “shrinkage” fixed-point estimator
Frédéric Pascal, Yihui Quek
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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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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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Shrinkage Estimators for Uplift Regression
2020Uplift modeling is an approach to machine learning which allows for predicting the net effect of an action (with respect to not taking the action). To achieve this, the training population is divided into two parts: the treatment group, which is subjected to the action, and the control group, on which the action is not taken. Our task is to construct a
Krzysztof Rudas, Szymon Jaroszewicz
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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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The distribution of stochastic shrinkage biasing parameters of the Liu type estimator
Applied Mathematics and Computation, 2005 The density function of the stochastic shrinkage parameters of the operational Liu type estimator is derived by assuming normality. The classical linear regression model (CLRM) is considered for the purpose.
Öztürk F., Akdeniz F.
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Prediction with shrinkage estimators
Series 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.
Goldstein, M., Brown, P. J.
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