A comparison of covariate adjustment approaches under model misspecification in individually randomized trials. [PDF]
Trials, 2023 Adjustment for baseline covariates in randomized trials has been shown to lead to gains in power and can protect against chance imbalances in covariates.
Tackney MS +5 more
europepmc +2 more sources
Learning Robust Statistics for Simulation-based Inference under Model Misspecification [PDF]
Neural Information Processing Systems, 2023 Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models. However, such methods
Daolang Huang +4 more
semanticscholar +1 more source
The Impact of Measurement Model Misspecification on Coefficient Omega Estimates of Composite Reliability. [PDF]
Educ Psychol Meas, 2023 Coefficient omega indices are model-based composite reliability estimates that have become increasingly popular. A coefficient omega index estimates how reliably an observed composite score measures a target construct as represented by a factor in a ...
Bell SM, Chalmers RP, Flora DB.
europepmc +2 more sources
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference [PDF]
arXiv.org, 2022 Aided by advances in neural density estimation, considerable progress has been made in recent years towards a suite of simulation-based inference (SBI) methods capable of performing flexible, black-box, approximate Bayesian inference for stochastic ...
P. Cannon +2 more
semanticscholar +1 more source
Measuring Model Misspecification: Application to Propensity Score Methods with Complex Survey Data. [PDF]
Comput Stat Data Anal, 2018 Lenis D, Ackerman B, Stuart EA.
europepmc +2 more sources
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks [PDF]
DAGM, 2021 Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains. In particular, the BayesFlow framework uses a two-step approach to enable amortized parameter estimation in
M. Schmitt +3 more
semanticscholar +1 more source
When models fail: An introduction to posterior predictive checks and model misspecification in gravitational-wave astronomy [PDF]
Publications Astronomical Society of Australia, 2022 Bayesian inference is a powerful tool in gravitational-wave astronomy. It enables us to deduce the properties of merging compact-object binaries and to determine how these mergers are distributed as a population according to mass, spin, and redshift.
I. Romero-Shaw, E. Thrane, P. Lasky
semanticscholar +1 more source
Likelihood ratio tests under model misspecification in high dimensions [PDF]
Journal of Multivariate Analysis, 2022 We investigate the likelihood ratio test for a large block-diagonal covariance matrix with an increasing number of blocks under the null hypothesis. While so far the likelihood ratio statistic has only been studied for normal populations, we establish ...
Nina Dörnemann
semanticscholar +1 more source
Learning under Distribution Mismatch and Model Misspecification [PDF]
International Symposium on Information Theory, 2021 We study learning algorithms when there is a mismatch between the distributions of the training and test datasets of a learning algorithm. The effect of this mismatch on the generalization error and model misspecification are quantified.
Mohammad Saeed Masiha +3 more
semanticscholar +1 more source
An improved multiply robust estimator for the average treatment effect
BMC Medical Research Methodology, 2023 Background In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). However,
Ce Wang +4 more
doaj +1 more source