Results 11 to 20 of about 30,695 (301)
Minimizing sensitivity to model misspecification [PDF]
Quantitative Economics, 2022 We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one‐step adjustments.
Weidner, Martin, Bonhomme, Stéphane
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Decision-Making Under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets [PDF]
EntropyDistributionally Robust Optimisation (DRO) protects risk-averse decision-makers by considering the worst-case risk within an ambiguity set of distributions based on the empirical distribution or a model. To further guard against finite, noisy data, model-
Charita Dellaporta +2 more
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Misspecification in Inverse Reinforcement Learning [PDF]
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function R from a policy pi. To do this, we need a model of how pi relates to R. In the current literature, the most common models are optimality, Boltzmann rationality, and causal entropy maximisation. One of the primary motivations behind IRL is to infer human preferences from human
Joar Skalse, Alessandro Abate
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Surveillance of Pharmaceutical Risk‐Mitigation Behavior: Applying and Comparing Statistical Process Control Methods Using Real World Data [PDF]
Learning Health SystemsIntroduction Active post‐marketing surveillance of prescribing behavior of high‐risk drugs may provide early warning of unforeseen issues in a population, yet analysis approaches for surveillance using real‐world data are underdeveloped.
Harris Butler +3 more
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Generalized Information Matrix Tests for Detecting Model Misspecification
Econometrics, 2016 Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in regression models in both randomized controlled trials and observational studies.
Richard M. Golden +2 more
exaly +3 more sources
Assessing the impact of variance heterogeneity and misspecification in mixed-effects location-scale models [PDF]
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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In models we trust: preregistration, large samples, and replication may not suffice
Frontiers in Psychology, 2023 Despite discussions about the replicability of findings in psychological research, two issues have been largely ignored: selection mechanisms and model assumptions.
Martin Spiess, Pascal Jordan
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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
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Axioms, 2022 Factor analysis is one of the most important statistical tools for analyzing multivariate data (i.e., items) in the social sciences. An essential case is the comparison of multiple groups on a one-dimensional factor variable that can be interpreted as a ...
Alexander Robitzsch
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Dynamic Concern for Misspecification [PDF]
Proceedings of the 24th ACM Conference on Economics and Computation, 2023 I consider an agent who posits a set of probabilistic models for the payoff‐relevant outcomes. The agent has a prior over this set but fears the actual model is omitted and hedges against this possibility. The concern for misspecification is endogenous: If a model explains the previous observations well, the concern attenuates.
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