Results 111 to 120 of about 13,398 (256)
Mitigating measurement error in misspecified small area models
Canadian Journal of Statistics, EarlyView.Abstract In the framework of Small area estimation, we consider an area‐level model where a subset of covariates is measured with error. The extent of the error is assumed to be constant throughout the areas, and it is expressed by a scalar parameter γ$$ \gamma $$, which multiplies the deterministic covariance matrix of the estimator of the true ...
Diego Battagliese +3 more
wiley +1 more source
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
doaj +1 more source
Robust Portfolio Optimization with Environmental, Social, and Corporate Governance Preference
RisksThis study addresses the crucial but under-explored topic of ambiguity aversion, i.e., model misspecification, in the area of environmental, social, and corporate governance (ESG) within portfolio decisions.
Marcos Escobar-Anel, Yiyao Jiao
doaj +1 more source
Newsvendor under Mean-Variance Ambiguity and Misspecification [PDF]
Consider a newsvendor problem with an unknown demand distribution. When addressing the issue of distributional uncertainty, we distinguish ambiguity under which the newsvendor does not differentiate demand distributions of common distributional ...
Chen, Zhi +3 more
core
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
openaire +2 more sources
Canadian Journal of Statistics, EarlyView.Abstract Irregular observation times are common in longitudinal observational studies and can affect causal inferences. We use data from electronic health records of Kaiser Permanente Washington patients in the United States who initiated an antidepressant medication between 2008 and 2018, with a confirming diagnosis of depression. We are interested in
Janie Coulombe +2 more
wiley +1 more source
How Robust is Robust Control in the Time Domain? [PDF]
By applying robust control the decision maker wants to make good decisions when his model is only a good approximation of the true one. Such decisions are said to be robust to model misspecification.
Marco P. Tucci
core
Model selection for causal inference in missing exposure problems
Canadian Journal of Statistics, EarlyView.Abstract In causal inference, properly selecting the propensity score (PS) model is an important topic and has been widely investigated in observational studies. However, there are very few studies investigating the model selection issue for causal inference when the exposure is missing at random (MAR). When the exposure variable contains missing data,
Yuliang Shi, Yeying Zhu, Joel A. Dubin
wiley +1 more source
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 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
doaj +1 more source