Results 241 to 250 of about 25,682 (291)
From bulk RNA sequencing to spatial transcriptomics: a comparative review of differential gene expression analysis methods. [PDF]
Hum GenomicsKalantari-Dehaghi M +2 more
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A semi-empirical Bayes approach for calibrating weak instrumental bias in sex-specific Mendelian randomization studies. [PDF]
Am J Hum GenetHuang YJ +9 more
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Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding. [PDF]
Lifetime Data AnalYe X, Yang S, Wang X, Liu Y.
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Statistical inference on high-dimensional covariate-dependent Gaussian graphical regressions. [PDF]
BiometricsMeng X, Zhang J, Li Y.
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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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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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Minimum Message Length shrinkage estimation
Statistics & Probability Letters, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Makalic, Enes, Schmidt, Daniel 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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