Results 81 to 90 of about 13,095 (150)
On the Model-Misspecification in Reinforcement Learning [PDF]
The success of reinforcement learning (RL) crucially depends on effective function approximation when dealing with complex ground-truth models. Existing sample-efficient RL algorithms primarily employ three approaches to function approximation: policy ...
Yang, Lin, Li, Yunfan
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BMC Medical Research MethodologyPurpose Linear Mixed Model (LMM) is a common statistical approach to model the relation between exposure and outcome while capturing individual variability through random effects.
Vincent Jeanselme +2 more
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Newsvendor under Ambiguity and Misspecification
Manufacturing & Service Operations ManagementWe consider a newsvendor problem with unknown demand distribution, where we distinguish ambiguity under which the newsvendor does not differentiate demand distributions of common characteristics (e.g., mean and variance) and misspecification under which such characteristics might be misspecified (due to, e.g., estimation error and/or distribution shift)
Feng Liu +3 more
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Market selection and learning under model misspecification
, 2023 This paper studies market selection in an Arrow-Debreu economy with complete markets where agents learn over misspecified models. Under model misspecification, standard Bayesian learning loses its formal justification and biased learning processes may ...
Bottazzi, Giulio +2 more
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Uncertainty quantification for misspecified machine learned interatomic potentials
npj Computational MaterialsThe use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now plausibly target quantitative predictions in a variety of settings, which ...
Danny Perez +3 more
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BMC Medical Research Methodology, 2018 Background Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors.
Wansu Chen +3 more
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Acta Universitatis Lodziensis. Folia Oeconomica, 2014 The aim of this paper is to evaluate the spatial and hierarchical models for data generating processes with spatial heterogeneity and spatial dependence at the higher level.
Edyta Łaszkiewicz
doaj
BMC Medical Research MethodologyBackground In multi-site studies in clinical practice, the treatment effects may exhibit potential heterogeneity across different sites. Additionally, propensity score methods used to adjust for confounding may suffer from model misspecification, which ...
Chen Huang +3 more
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No-Regret Linear Bandits under Gap-Adjusted Misspecification [PDF]
This work studies linear bandits under a new notion of gap-adjusted misspecification and is an extension of Liu et al. (2023). When the underlying reward function is not linear, existing linear bandits work usually relies on a uniform misspecification ...
Liu, Chong +4 more
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Investigating item complexity as a source of cross-national DIF in TIMSS math and science
Large-scale Assessments in EducationBackground Large scale international assessments depend on invariance of measurement across countries. An important consideration when observing cross-national differential item functioning (DIF) is whether the DIF actually reflects a source of bias, or ...
Qi Huang, Daniel M. Bolt, Weicong Lyu
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