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64 results on '"Roos, Małgorzata"'

1. Quantification of empirical determinacy: the impact of likelihood weighting on posterior location and spread in Bayesian meta-analysis estimated with JAGS and INLA

Author

Hunanyan, Sona, Rue, Håvard, Plummer, Martyn, and Roos, Małgorzata

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

The popular Bayesian meta-analysis expressed by Bayesian normal-normal hierarchical model (NNHM) synthesizes knowledge from several studies and is highly relevant in practice. Moreover, NNHM is the simplest Bayesian hierarchical model (BHM), which illustrates problems typical in more complex BHMs. Until now, it has been unclear to what extent the data determines the marginal posterior distributions of the parameters in NNHM. To address this issue we computed the second derivative of the Bhattacharyya coefficient with respect to the weighted likelihood, defined the total empirical determinacy (TED), the proportion of the empirical determinacy of location to TED (pEDL), and the proportion of the empirical determinacy of spread to TED (pEDS). We implemented this method in the R package \texttt{ed4bhm} and considered two case studies and one simulation study. We quantified TED, pEDL and pEDS under different modeling conditions such as model parametrization, the primary outcome, and the prior. This clarified to what extent the location and spread of the marginal posterior distributions of the parameters are determined by the data. Although these investigations focused on Bayesian NNHM, the method proposed is applicable more generally to complex BHMs., Comment: 22 pages, 1 figure

Published
2021

3. Sensitivity analysis for Bayesian hierarchical models

Author

Roos, Malgorzata, Martins, Thiago G., Held, Leonhard, and Rue, Havard

Subjects
Statistics - Methodology
Abstract

Prior sensitivity examination plays an important role in applied Bayesian analyses. This is especially true for Bayesian hierarchical models, where interpretability of the parameters within deeper layers in the hierarchy becomes challenging. In addition, lack of information together with identifiability issues may imply that the prior distributions for such models have an undesired influence on the posterior inference. Despite its relevance, informal approaches to prior sensitivity analysis are currently used. They require repetitive re-runs of the model with ad-hoc modified base prior parameter values. Other formal approaches to prior sensitivity analysis suffer from a lack of popularity in practice, mainly due to their high computational cost and absence of software implementation. We propose a novel formal approach to prior sensitivity analysis which is fast and accurate. It quantifies sensitivity without the need for a model re-run. We develop a ready-to-use priorSens package in R for routine prior sensitivity investigation by R-INLA. Throughout a series of examples we show how our approach can be used to detect high prior sensitivities of some parameters as well as identifiability issues in possibly over-parametrized Bayesian hierarchical models., Comment: 25 pages, 3 figures

Published
2013

11. Quantification of Empirical Determinacy: The Impact of Likelihood Weighting on Posterior Location and Spread in Bayesian Meta-Analysis Estimated with JAGS and INLA.

Author

Hunanyan, Sona, Rue, Håvard, Plummer, Martyn, and Roos, Małgorzata

Subjects
BAYESIAN analysis, META-analysis, COEFFICIENTS (Statistics), HIERARCHICAL Bayes model, EMPIRICAL research
Abstract

The popular Bayesian meta-analysis expressed by the normal-normal hierarchical model synthesizes knowledge from several studies and is highly relevant in practice. The normal-normal hierarchical model is the simplest Bayesian hierarchical model, but illustrates problems typical in more complex Bayesian hierarchical models. Until now, it has been unclear to what extent the data determines the marginal posterior distributions of the parameters in the normal-normal hierarchical model. To address this issue we computed the second derivative of the Bhattacharyya coefficient with respect to the weighted likelihood. This quantity, which we define as the total empirical determinacy (TED), can be written as the sum of two terms: the empirical determinacy of location (EDL), and the empirical determinacy of spread (EDS). We implemented this method in the R package ed4bhm and considered two case studies and one simulation study. We quantified TED, EDL and EDS under different modeling conditions such as model parametrization, the primary outcome, and the prior. This clarifies to what extent the location and spread of the marginal posterior distributions of the parameters are determined by the data. Although these investigations focused on Bayesian normal-normal hierarchical model, the method proposed is applicable more generally to complex Bayesian hierarchical models. [ABSTRACT FROM AUTHOR]

Published
2023
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16. Establishing effective cross-disciplinary collaboration: Combining simple rules for reproducible computational research, a good data management plan, and good research practice.

Author

Stawarczyk, Bogna and Roos, Małgorzata

Subjects
*DATA management, *REPRODUCIBLE research, *DENTAL materials, *COMPUTER operating systems, *APPLIED sciences
Abstract

Our project uses GitHub (https://github.com/) to assure version control and Zenodo (https://zenodo.org/) to assure time stamping of datasets, software, and reports. The communication between both equal partners in our project is supported by rules R1-R4, which apply to data comprising measurements, meta-data, software, software documentation, and dynamic reports. [Extracted from the article]

Published
2023
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