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Modeling Model Misspecification in Structural Equation Models [PDF]
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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Identifying the sources of model misspecification [PDF]
Journal of Monetary Economics, 2020 The first and third authors acknowledge financial support from the National Science Foundation through grants 102159 and 1022125, respectively.
Atsushi Inoue +2 more
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Errors in Statistical Inference Under Model Misspecification: Evidence, Hypothesis Testing, and AIC [PDF]
Frontiers in Ecology and Evolution, 2019 The methods for making statistical inferences in scientific analysis have diversified even within the frequentist branch of statistics, but comparison has been elusive.
Brian Dennis +4 more
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Dynamic Learning and Pricing with Model Misspecification [PDF]
Management Science, 2019 We study a multiperiod dynamic pricing problem with contextual information, where the seller uses a misspecified demand model. The seller sequentially observes past demand, updates model parameters, and then chooses the price for the next period based on time-varying features.
Mila Nambiar +2 more
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Genetic model misspecification in genetic association studies [PDF]
BMC Research Notes, 2017 Objective The underlying model of the genetic determinant of a trait is generally not known with certainty a priori. Hence, in genetic association studies, a dominant model might be erroneously modelled as additive, an error investigated previously.
Amadou Gaye, Sharon K. Davis
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Quantification of model risk that is caused by model misspecification. [PDF]
J Appl Stat, 2022 In this paper, we suggest a technique to quantify model risk, particularly model misspecification for binary response regression problems found in financial risk management, such as in credit risk modelling. We choose the probability of default model as one instance of many other credit risk models that may be misspecified in a financial institution ...
Seitshiro MB, Mashele HP.
europepmc +3 more sources
Minimum Penalized ϕ-Divergence Estimation under Model Misspecification [PDF]
Entropy, 2018 This paper focuses on the consequences of assuming a wrong model for multinomial data when using minimum penalized ϕ -divergence, also known as minimum penalized disparity estimators, to estimate the model parameters.
M. Virtudes Alba-Fernández +2 more
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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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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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On measuring sensitivity to parametric model misspecification
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2001 Summary In settings where parametric inference is inconsistent under model misspecification, the discrepancy between correct and misspecified inferences is compared with the discrepancy between correct and misspecified models. To make the comparison tractable, large sample and small misspecification approximations are employed. The ratio
Paul Gustafson
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