English
Related papers

Related papers: Bayes factors for accelerated life testing models

200 papers

We propose a new model selection method, the posterior averaging information criterion, for Bayesian model assessment from a predictive perspective. The theoretical foundation is built on the Kullback-Leibler divergence to quantify the…

Methodology · Statistics 2020-09-22 Shouhao Zhou

A new methodology for model determination in decomposable graphical Gaussian models is developed. The Bayesian paradigm is used and, for each given graph, a hyper inverse Wishart prior distribution on the covariance matrix is considered.…

Computation · Statistics 2015-03-13 Sophie Donnet , Jean-Michel Marin

This paper develops a methodology for robust Bayesian inference through the use of disparities. Metrics such as Hellinger distance and negative exponential disparity have a long history in robust estimation in frequentist inference. We…

Methodology · Statistics 2012-11-28 Giles Hooker , Anand Vidyashankar

Despite major methodological developments, Bayesian inference for Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a method to infer the marginal and…

Methodology · Statistics 2018-04-10 Gwenaël G. R. Leday , Sylvia Richardson

Multilevel models (MLMs) are a central building block of the Bayesian workflow. They enable joint, interpretable modeling of data across hierarchical levels and provide a fully probabilistic quantification of uncertainty. Despite their…

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

We propose optimal Bayesian two-sample tests for testing equality of high-dimensional mean vectors and covariance matrices between two populations. In many applications including genomics and medical imaging, it is natural to assume that…

Methodology · Statistics 2021-12-07 Kyoungjae Lee , Kisung You , Lizhen Lin

We study Bayesian methods for large-scale linear inverse problems, focusing on the challenging task of hyperparameter estimation. Typical hierarchical Bayesian formulations that follow a Markov Chain Monte Carlo approach are possible for…

Numerical Analysis · Mathematics 2024-01-05 Khalil A Hall-Hooper , Arvind K Saibaba , Julianne Chung , Scot M Miller

Approximate Bayesian computation (ABC) methods can be used to sample from posterior distributions when the likelihood function is unavailable or intractable, as is often the case in biological systems. ABC methods suffer from inefficient…

Machine Learning · Statistics 2019-12-03 Charlie Rogers-Smith , Henri Pesonen , Samuel Kaski

In Bayesian statistics, the marginal likelihood is used for model selection and averaging, yet it is often challenging to compute accurately for complex models. Approaches such as bridge sampling, while effective, may suffer from issues of…

Methodology · Statistics 2026-02-24 Giorgio Micaletto , Aki Vehtari

The prevalence of one-shot devices is quite prolific in engineering and medical domains. Unlike typical one-shot devices, nondestructive one-shot devices (NOSD) may survive multiple tests and offer additional data for reliability…

Methodology · Statistics 2025-02-11 Shanya Baghel , Shuvashree Mondal

This paper introduces a feasible and practical Bayesian method for unit root testing in financial time series. We propose a convenient approximation of the Bayes factor in terms of the Bayesian Information Criterion as a straightforward and…

Econometrics · Economics 2021-02-23 Magris Martin , Iosifidis Alexandros

Typical Bayesian methods for models with latent variables (or random effects) involve directly sampling the latent variables along with the model parameters. In high-level software code for model definitions (using, e.g., BUGS, JAGS, Stan),…

Computation · Statistics 2022-12-12 E. C. Merkle , D. Furr , S. Rabe-Hesketh

Competing risk models are survival models with several events of interest acting in competition and whose occurrence is only observed for the event that occurs first in time. This paper presents a Bayesian approach to these models in which…

Composite likelihood provides approximate inference when the full likelihood is intractable and sub-likelihood functions of marginal events can be evaluated relatively easily. It has been successfully applied for many complex models.…

Methodology · Statistics 2024-09-05 Wentao Li , Rosabeth White , Dennis Prangle

Bayesian analysis often concerns an evaluation of models with different dimensionality as is necessary in, for example, model selection or mixture models. To facilitate this evaluation, transdimensional Markov chain Monte Carlo (MCMC)…

Methodology · Statistics 2018-08-13 Daniel W. Heck , Antony M. Overstall , Quentin F. Gronau , Eric-Jan Wagenmakers

Bayesian linear mixed-effects models and Bayesian ANOVA are increasingly being used in the cognitive sciences to perform null hypothesis tests, where a null hypothesis that an effect is zero is compared with an alternative hypothesis that…

Methodology · Statistics 2023-08-15 Daniel J. Schad , Bruno Nicenboim , Shravan Vasishth

Posterior distributions often feature intractable normalizing constants, called marginal likelihoods or evidence, that are useful for model comparison via Bayes factors. This has motivated a number of methods for estimating ratios of…

Computation · Statistics 2018-10-03 Maxime Rischard , Pierre E. Jacob , Natesh Pillai

Comparing competing mathematical models of complex natural processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for…

We propose an iterative variable selection method for the accelerated failure time model using high-dimensional survival data. Our method pioneers the use of the recently proposed structured screen-and-select framework for survival…

Methodology · Statistics 2025-03-04 Nilotpal Sanyal