Aversion to ambiguity and model misspecification in dynamic stochastic environments. [PDF]
Proc Natl Acad Sci U S A, 2018 Preferences that accommodate aversion to subjective uncertainty and its potential misspecification in dynamic settings are a valuable tool of analysis in many disciplines.
Hansen LP, Miao J.
europepmc +5 more sources
Confronting model misspecification in macroeconomics [PDF]
Journal of Econometrics, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel F. Waggoner, Tao Zha
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Bayesian graph selection consistency under model misspecification. [PDF]
Bernoulli (Andover), 2021 Gaussian graphical models are a popular tool to learn the dependence structure in the form of a graph among variables of interest. Bayesian methods have gained in popularity in the last two decades due to their ability to simultaneously learn the covariance and the graph and characterize uncertainty in the selection. For scalability of the Markov chain
Niu Y, Pati D, Mallick BK.
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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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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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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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Misspecification in Mixed-Model-Based Association Analysis [PDF]
Genetics, 2015 Abstract Additive genetic variance in natural populations is commonly estimated using mixed models, in which the covariance of the genetic effects is modeled by a genetic similarity matrix derived from a dense set of markers. An important but usually implicit assumption is that the presence of any nonadditive genetic effect increases ...
Kruijer, Willem
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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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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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