Results 31 to 40 of about 1,563,284 (329)
Robust Bayesian meta-analysis: Addressing publication bias with model-averaging.
Psychological methods, 2022 Meta-analysis is an important quantitative tool for cumulative science, but its application is frustrated by publication bias. In order to test and adjust for publication bias, we extend model-averaged Bayesian meta-analysis with selection models.
Maximilian Maier +2 more
semanticscholar +1 more source
Forecasting in dynamic factor models using Bayesian model averaging [PDF]
, 2004 This paper considers the problem of forecasting in dynamic factor models using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given.
Bai +36 more
core +1 more source
Application of Bayesian model averaging to measurements of the primordial power spectrum [PDF]
, 2010 Cosmological parameter uncertainties are often stated assuming a particular model, neglecting the model uncertainty, even when Bayesian model selection is unable to identify a conclusive best model.
Andrew R. Liddle +6 more
core +2 more sources
BMC Medical Research Methodology, 2010 Background Automatic variable selection methods are usually discouraged in medical research although we believe they might be valuable for studies where subject matter knowledge is limited.
Steineck Gunnar +3 more
doaj +1 more source
Bayesian model averaging: improved variable selection for matched case-control studies
Epidemiology, Biostatistics and Public Health, 2022 Background: The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field.
Yi Mu, Isaac See, Jonathan R. Edwards
doaj
Research Synthesis Methods, 2022 Publication bias is a ubiquitous threat to the validity of meta‐analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation ...
František Bartoš +4 more
semanticscholar +1 more source
Semiparametric GARCH via Bayesian Model Averaging [PDF]
Journal of Business & Economic Statistics, 2019 As the dynamic structure of the financial markets is subject to dramatic changes, a model capable of providing consistently accurate volatility estimates must not make strong assumptions on how prices change over time. Most volatility models impose a particular parametric functional form that relates an observed price change to a volatility forecast ...
Wilson Ye Chen, Richard H. Gerlach
openaire +2 more sources
Bayesian weighting of climate models based on climate sensitivity
Communications Earth & Environment, 2023 Using climate model ensembles containing members that exhibit very high climate sensitivities to increasing CO2 concentrations can result in biased projections.
Elias C. Massoud +3 more
doaj +1 more source
Bayesian model averaging for multivariate extremes [PDF]
Extremes, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sabourin, Anne +2 more
openaire +3 more sources
Bayesian Model Averaging and Jointness Measures for gretl
Journal of Statistical Software, 2015 This paper presents a software package that implements Bayesian model averaging for gretl, the GNU regression, econometrics and time-series library.
Marcin Błażejowski, Jacek Kwiatkowski
doaj +1 more source