Results 11 to 20 of about 13,855,951 (296)
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
core +10 more sources
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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Model Misspecification and Underdiversification
The Journal of Finance, 2001 AbstractIn this paper, we study intertemporal portfolio choice when an investor accounts explicitly for model misspecification. We develop a framework that allows for ambiguity about not just the joint distribution of returns for all stocks in the portfolio, but also for different levels of ambiguity for the marginal distribution of returns for any ...
Uppal, Raman, Wang, Tan
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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
doaj +2 more sources
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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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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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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Measuring Model Misspecification: Application to Propensity Score Methods with Complex Survey Data. [PDF]
Comput Stat Data Anal, 2018 Lenis D, Ackerman B, Stuart EA.
europepmc +2 more sources
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
An improved multiply robust estimator for the average treatment effect
BMC Medical Research Methodology, 2023 Background In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). However,
Ce Wang +4 more
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