Results 21 to 30 of about 21,776,804 (321)
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
On Lagrange Multiplier Tests in Multidimensional Item Response Theory: Information Matrices and Model Misspecification. [PDF]
Educ Psychol Meas, 2018 Falk CF, Monroe S.
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
Marvin Schmitt +3 more
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
A view on model misspecification in uncertainty quantification [PDF]
BNAIC/BENELEARN, 2022 Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model
Yuko Kato, D. Tax, M. Loog
semanticscholar +1 more source
Stable Prediction with Model Misspecification and Agnostic Distribution Shift [PDF]
AAAI Conference on Artificial Intelligence, 2020 For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified.
Kun Kuang +4 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
Statistical Modelling, 2008 A common problem in statistical modelling is to distinguish between finite mixture distribution and a homogeneous non-mixture distribution. Finite mixture models are widely used in practice and often mixtures of normal densities are indistinguishable from homogenous non-normal densities.
Tarpey, Thaddeus +2 more
openaire +3 more sources
Model Misspecification as the Causes of Flypaper Effect
Proceedings, 2023 The aim of this paper is to investigate the relationship between the Fly-paper effect (FPE) and possible errors in the specification of econometric models used in the empirical analysis of FPE.
Siniša Mali
doaj +1 more source
Mathematics, 2021 In optimal experimental design theory it is usually assumed that the response variable follows a normal distribution with constant variance. However, some works assume other probability distributions based on additional information or practitioner’s ...
Sergio Pozuelo-Campos +2 more
doaj +1 more source