Results 11 to 20 of about 29,799 (296)
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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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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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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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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Optimal weighting for estimating generalized average treatment effects
Journal of Causal Inference, 2022 In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects.
Kallus Nathan, Santacatterina Michele
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Entropy, 2020 We consider selection of random predictors for a high-dimensional regression problem with a binary response for a general loss function. An important special case is when the binary model is semi-parametric and the response function is misspecified under
Mariusz Kubkowski, Jan Mielniczuk
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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
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Inference for Iterated GMM Under Misspecification [PDF]
, 2021 This paper develops inference methods for the iterated overidentified Generalized Method of Moments (GMM) estimator. We provide conditions for the existence of the iterated estimator and an asymptotic distribution theory, which allows for mild ...
Hansen, BE +3 more
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Contemporary Clinical Trials Communications, 2021 Covariate constrained randomization (CCR) is a method of controlling imbalance in important baseline covariates in cluster-randomized trials (CRT). We use simulated CRTs to investigate the performance (control of imbalance) of CCR relative to simple ...
Madeleine Organ +5 more
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