Results 1 to 10 of about 2,456,967 (206)
Two modeling strategies for empirical Bayes estimation. [PDF]
Stat Sci, 2014 Empirical Bayes methods use the data from parallel experiments, for instance observations Xk ~ 𝒩 (Θ k , 1) for k = 1, 2, …, N, to estimate the conditional distributions Θ k |Xk .
Efron B.
europepmc +3 more sources
Empirical Bayes Conditional Density Estimation
Statistica, 2015 The problem of nonparametric estimation of the conditional density of a response, given a vector of explanatory variables, is classical and of prominent importance in many prediction problems since the conditional density provides a more comprehensive ...
Catia Scricciolo
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Robust Bayes-like estimation: Rho-Bayes estimation [PDF]
The Annals of Statistics, 2020 68 ...
Baraud, Yannick, Birgé, Lucien
openaire +6 more sources
Likelihood-Free Parameter Estimation with Neural Bayes Estimators [PDF]
American Statistician, 2022 Neural Bayes estimators are neural networks that approximate Bayes estimators. They are fast, likelihood-free, and amenable to rapid bootstrap-based uncertainty quantification.
Matthew Sainsbury-Dale +2 more
semanticscholar +1 more source
Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations
Mathematics, 2023 Generalized Bayes is a Bayesian study based on a learning rate parameter. This paper considers a generalized Bayes estimation to study the effect of the learning rate parameter on the estimation results based on a joint censored sample of type-II ...
Y. Abdel-Aty +2 more
semanticscholar +1 more source
Media Statistika, 2021 Small Area Estimation (SAE) is one of the statistical methods that used to estimate parameters of model from small subpopulations. Because of that, additional information is needed to predict these parameters that will result a more accurate predictive ...
Ferra Yanuar +2 more
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On F-modeling based Empirical Bayes Estimation of Variances [PDF]
Biometrika, 2018 We consider the problem of empirical Bayes estimation of multiple variances when provided with sample variances. Assuming an arbitrary prior on the variances, we derive different versions of the Bayes estimators using different loss functions.
Yeil Kwon, Zhigen Zhao
semanticscholar +1 more source
Media Statistika, 2020 One distribution of rainfall data is a lognormal distribution with location parameters and scale parameters . This study aims to estimate the mean and variance of rainfall data in several selected cities and regencies in West Sumatra.
Ferra Yanuar +2 more
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The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and Its Bayes Estimation
Energies, 2022 This paper proposes the Compound Inverse Rayleigh distribution as a proper model for the characterization of the probability distribution of extreme values of wind-speed.
Elio Chiodo +2 more
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deconvolveR: A G-Modeling Program for Deconvolution and Empirical Bayes Estimation
Journal of Statistical Software, 2020 Empirical Bayes inference assumes an unknown prior density g(θ) has yielded (unobservables) Θ 1 , Θ 2 , ..., Θ N , and each Θ i produces an independent observation X i from p i (X i | Θ i ). The marginal density f i (X i ) is a convolution of the prior g
B. Narasimhan, B. Efron
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