Results 41 to 50 of about 16,361 (164)
Target Matrix Estimators in Risk-Based Portfolios
Risks, 2018 Portfolio weights solely based on risk avoid estimation errors from the sample mean, but they are still affected from the misspecification in the sample covariance matrix.
Marco Neffelli
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Misspecification in Inverse Reinforcement Learning
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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Multicollinearity and Model Misspecification
Sociological Science, 2016 Multicollinearity in linear regression is typically thought of as a problem of large standard errors due to near-linear dependencies among independent variables. This problem can be solved by more informative data, possibly in the form of a larger sample.
Christopher Winship, Bruce Western
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Modeling Model Misspecification in Structural Equation Models
Stats, 2023 Structural equation models constrain mean vectors and covariance matrices and are frequently applied in the social sciences. Frequently, the structural equation model is misspecified to some extent.
Alexander Robitzsch
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Specifying Turning Point in Piecewise Growth Curve Models: Challenges and Solutions
Frontiers in Applied Mathematics and Statistics, 2017 Piecewise growth curve model (PGCM) is often used when the underlying growth process is not linear and is hypothesized to consist of phasic developments connected by turning points (or knots or change points).
Ling Ning, Wen Luo
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System Misspecification Testing and Structural Change in the Demand for Meats
Journal of Agricultural and Resource Economics, 1995 A misspecification testing strategy designed to ensure that the statistical assumptions underlying a system of equations are appropriate is outlined. The system tests take into account information in, and interactions between, all equations in the system
Anya M. McGuirk +3 more
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Fit assessment and selection between competitive models in sem
Statistica, 2007 This paper outlines some issues in fit assessment and in selection between competitive models in Structural Equation Models(SEM). The theory on chi-square statistic and the results of some simulation studies are reviewed and the methodological ...
Roberto Di Natale
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Power Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial
Advances in Methods and Practices in Psychological Science, 2021 Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM.
Y. Andre Wang, Mijke Rhemtulla
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On the Model-Misspecification in Reinforcement Learning
, 2023 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-based, value-based, and model-based methods.
Yunfan Li, Lin Yang
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A View on Model Misspecification in Uncertainty Quantification
, 2023 An initial version of the current work has been accepted to be presented at BNAIC/BeNeLearn 2022, to which it was submitted on August 27 ...
Yuko Kato, David M. J. Tax, Marco Loog
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