Results 241 to 250 of about 25,719 (290)

Evaluation of Methods Adjusting for Unmeasured Confounding Using Large Healthcare Databases: An Empirical Study Concerning Drugs Inducing Prematurity. [PDF]

Clin Transl Sci
Duong CH, Escolano S, Demailly R, Thiebaut A, Cottenet J, Quantin C, Tubert-Bitter P, Ahmed I.   +7 more
europepmc   +1 more source

Denoising: a powerful building block for imaging, inverse problems and machine learning. [PDF]

Philos Trans A Math Phys Eng Sci
Milanfar P, Delbracio M.
europepmc   +1 more source

Mapping the Analytical Landscape of Gene-Diet Interactions in Epidemiology: From Classical Models to Causal and Multi-Omics Frameworks. [PDF]

Nutrients
Maugeri A.
europepmc   +1 more source

Shrinkage estimation in prediction.

, 1997
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Interval Shrinkage Estimators

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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Minimum Message Length shrinkage estimation

Statistics & Probability Letters, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Makalic, Enes, Schmidt, Daniel F.
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Prediction with shrinkage estimators

Series Statistics, 1978
It 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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Multi-Index Shrinkage Estimation

The Journal of Wealth Management, 2005
The authors present an improved method for estimating the asset class covariance matrix for input into a mean variance optimizer. Starting with the Ledoit and Wolf [2003] stock level Bayesian shrinkage estimator, they derive a multi-index shrinkage estimator for capturing the actual asset class return structure and for estimating the covariance matrix.
Michael D. Bergmann, C. Thomas Howard
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