نتایج جستجو برای: james stein estimator
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Abstract— For audio denoising, diagonal thresholding estimators of spectrogram coefficients produce a “musical noise” that degrades audio perception. We introduce a block thresholding which produces hardly any musical noise and improves the SNR compared to diagonal thresholdings or Ephraim and Malah estimators. Spectrogram coefficients are grouped into blocks to compute attenuation factors. Thi...
In a system of regression models, finding feasible shrinkage is demanding since the covariance structure unknown and cannot be ignored. On other hand, specifying sub-space restrictions for adequate vital. This study proposes estimation strategies where restriction obtained from LASSO. Therefore, some LASSO-based Stein-type estimators are introduced, their asymptotic performance studied. Extensi...
Posterior expectation is a well-accepted method for data analysis via Bayesian inference based on parametric likelihoods. In this paper we propose utilizing empirical likelihood (EL) methodology to develop novel nonparametric posterior expectation. The parametric Bayesian methodology contains the empirical Bayes approach for the purpose of using the observed data to estimate parameters, or even...
The problem of estimating the shift (or, equivalently, the center of symmetry) of an unknown symmetric and periodic function f observed in Gaussian white noise is considered. Using the blockwise Stein method, a penalized profile likelihood with a data-driven penalization is introduced so that the estimator of the center of symmetry is defined as the maximizer of the penalized profile likelihood...
Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and practice. We present a formula precisely delineating the allowable degree of of undersampling of generalized sparse objects. The formula applies to Approximate Message Passing (AMP) algorithms for ...
A mental health trial is analyzed using a dose-response model, in which the number of sessions attended by the patients is deemed indicative of the dose of psychotherapeutic treatment. Here, the parameter of interest is the difference in causal treatment effects between the subpopulations that take part in different numbers of therapy sessions. For this data set, interactions between random tre...
Martin T. Stein MD,* James M. Perrin, MD Introduction Attention-deficit/hyperactivity disorder (ADHD) is one of the most common chronic conditions of childhood and the most common neurobehavioral disorder in child health. This article describes the developmental process and content of two recent American Academy of Pediatrics (AAP) Clinical Practice Guidelines: Diagnosis and Evaluation of the C...
April J. Ho, Jason L. Stein, Xue Hua, Suh Lee, Derrek P. Hibar, Alex D. Leow, Ivo D. Dinov, Arthur W. Toga, Andrew J. Saykin, Li Shen, Tatiana Foroud, Nathan Pankratz, Matthew J. Huentelman, David W. Craig, Jill D. Gerber, April N. Allen, Jason J. Corneveaux, Dietrich A. Stephan, Charles S. DeCarli, Bryan M. DeChairo, Steven G. Potkini, Clifford R. Jack, Jr., Michael W. Weiner, Cyrus A. Raji, O...
We present a multi-task learning approach to jointly estimate the means of multiple independent data sets. The proposed multi-task averaging (MTA) algorithm results in a convex combination of the single-task averages. We derive the optimal amount of regularization, and show that it can be effectively estimated. Simulations and real data experiments demonstrate that MTA outperforms both maximum ...
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