Results 11 to 20 of about 15,975 (254)
On the robustness of the adaptive lasso to model misspecification. [PDF]
Biometrika, 2012 Penalization methods have been shown to yield both consistent variable selection and oracle parameter estimation under correct model specification. In this article, we study such methods under model misspecification, where the assumed form of the regression function is incorrect, including generalized linear models for uncensored outcomes and the ...
Lu W, Goldberg Y, Fine JP.
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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
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Measuring model misspecification: Application to propensity score methods with complex survey data [PDF]
Computational Statistics and Data Analysis, 2018 Elizabeth Stuart, Benjamin Ackerman
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Aversion to ambiguity and model misspecification in dynamic stochastic environments [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2018 Lars Hansen +2 more
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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
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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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Statistical Modelling, 2008 A common problem in statistical modelling is to distinguish between finite mixture distribution and a homogeneous non-mixture distribution. Finite mixture models are widely used in practice and often mixtures of normal densities are indistinguishable from homogenous non-normal densities.
Tarpey, Thaddeus +2 more
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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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Structured ambiguity and model misspecification
Journal of Economic Theory, 2022 A decision maker is averse to not knowing a prior over a set of restricted structured models (ambiguity) and suspects that each structured model is misspecified. The decision maker evaluates intertemporal plans under all of the structured models and, to recognize possible misspecifications, under unstructured alternatives that are statistically close ...
Lars Peter Hansen, Thomas J. Sargent
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Robust control and model misspecification [PDF]
Journal of Economic Theory, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lars Peter Hansen +3 more
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