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We develop an empirical Bayes procedure for estimating the cell means in an unbalanced, two-way additive model with fixed effects. We employ a hierarchical model, which reflects exchangeability of the effects within treatment and within…

Methodology · Statistics 2016-05-30 Lawrence D. Brown , Gourab Mukherjee , Asaf Weinstein

Using a collection of simulated an real benchmarks, we compare Bayesian and frequentist regularization approaches under a low informative constraint when the number of variables is almost equal to the number of observations on simulated and…

Methodology · Statistics 2015-03-17 Gilles Celeux , Mohammed El Anbari , Jean-Michel Marin , Christian P. Robert

The parametric bootstrap can be used for the efficient computation of Bayes posterior distributions. Importance sampling formulas take on an easy form relating to the deviance in exponential families and are particularly simple starting…

Applications · Statistics 2013-01-15 Bradley Efron

We propose a model of inference and heuristic decision-making in groups that is rooted in the Bayes rule but avoids the complexities of rational inference in partially observed environments with incomplete information, which are…

Multiagent Systems · Computer Science 2016-11-04 M. Amin Rahimian , Ali Jadbabaie

Jeffreys-Lindley paradox is a case where frequentist and Bayesian hypothesis testing methodologies contradict with each other. This has caused confusion among data analysts for selecting a methodology for their statistical inference tasks.…

Other Statistics · Statistics 2025-03-20 Priyantha Wijayatunga

Modern scientific technology has provided a new class of large-scale simultaneous inference problems, with thousands of hypothesis tests to consider at the same time. Microarrays epitomize this type of technology, but similar situations…

Statistics Theory · Mathematics 2007-11-06 Bradley Efron

Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We…

Machine Learning · Computer Science 2016-11-23 Giorgio Corani , Alessio Benavoli , Janez Demšar , Francesca Mangili , Marco Zaffalon

Nonlinear mixed effects models have become a standard platform for analysis when data is in the form of continuous and repeated measurements of subjects from a population of interest, while temporal profiles of subjects commonly follow a…

Methodology · Statistics 2022-03-04 Se Yoon Lee

Neural Networks (NNs) have provided state-of-the-art results for many challenging machine learning tasks such as detection, regression and classification across the domains of computer vision, speech recognition and natural language…

Machine Learning · Statistics 2026-04-21 Ethan Goan , Clinton Fookes

Bayesian and frequentist criteria fundamentally differ, but often posterior and sampling distributions agree asymptotically (e.g., Gaussian with same covariance). For the corresponding single-draw experiment, we characterize the frequentist…

Statistics Theory · Mathematics 2024-07-04 David M. Kaplan , Longhao Zhuo

A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the…

Statistics Theory · Mathematics 2013-02-21 Andrew Gelman , Cosma Rohilla Shalizi

The frequentist interpretation of measurement results requires the specification of an ensemble of independent replications of the same experiment. For complex calculations of bias, coverage, significance, etc., this ensemble is often…

Data Analysis, Statistics and Probability · Physics 2014-11-18 Luc Demortier

In this paper we adopt the familiar sparse, high-dimensional linear regression model and focus on the important but often overlooked task of prediction. In particular, we consider a new empirical Bayes framework that incorporates data in…

Statistics Theory · Mathematics 2020-07-28 Ryan Martin , Yiqi Tang

Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical reasons, frequentist statistics has dominated data analysis in the past; but Bayesian statistics is making a comeback at the forefront of science.…

Software Engineering · Computer Science 2016-08-30 Carlo A. Furia

It is generally appreciated that a frequentist analysis of a group sequential trial must in order to avoid inflating type I error account for the fact that one or more interim analyses were performed. It is also to a lesser extent realised…

Methodology · Statistics 2025-12-12 Simon Bang Kristensen , Erik Thorlund Parner

We have developed a frequentist approach for model selection which determines the consistency between any cosmological model and the data using the distribution of likelihoods from the iterative smoothing method. Using this approach, we…

Cosmology and Nongalactic Astrophysics · Physics 2022-03-30 Hanwool Koo , Ryan E. Keeley , Arman Shafieloo , Benjamin L'Huillier

We propose a frequentist testing procedure that maintains a defined coverage and is optimal in the sense that it gives maximal power to detect deviations from a null hypothesis when the alternative to the null hypothesis is sampled from a…

Applications · Statistics 2020-07-07 Christian Bartels , Johanna Mielke , Ekkehard Glimm

Analogues of the frequentist chi-square and F tests are proposed for testing goodness-of-fit and consistency for Bayesian models. Simple examples exhibit these tests' detection of inconsistency between consecutive experiments with identical…

Instrumentation and Methods for Astrophysics · Physics 2016-03-16 L. B. Lucy

Nested error regression models are useful tools for analysis of grouped data, especially in the case of small area estimation. This paper suggests a nested error regression model using uncertain random effects in which the random effect in…

Methodology · Statistics 2017-02-28 Shonosuke Sugasawa , Tatsuya Kubokawa

Comment on ``Microarrays, Empirical Bayes and the Two-Groups Model'' [arXiv:0808.0572]

Methodology · Statistics 2008-08-06 Kenneth Rice , David Spiegelhalter