Results 11 to 20 of about 21,776,804 (321)
Making Decisions under Model Misspecification [PDF]
SSRN Electronic Journal, 2020 We use decision theory to confront uncertainty that is sufficiently broad to incorporate "models as approximations." We presume the existence of a featured collection of what we call "structured models" that have explicit substantive motivations.
S. Cerreia-Vioglio +3 more
semanticscholar +4 more sources
Estimation Under Model Misspecification With Fake Features [PDF]
IEEE Transactions on Signal Processing, 2022 We consider estimation under model misspecification where there is a model mismatch between the underlying system, which generates the data, and the model used during estimation.
Martin Hellkvist +2 more
semanticscholar +5 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
doaj +3 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
doaj +2 more sources
Minimizing Sensitivity to Model Misspecification [PDF]
Quantitative Economics, 2018 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
M. Weidner, S. Bonhomme
semanticscholar +9 more sources
Confronting model misspecification in macroeconomics [PDF]
Journal of Econometrics, 2012 We estimate a Markov-switching mixture of two familiar macroeconomic models: a richly parameterized dynamic stochastic general equilibrium (DSGE) model and a corresponding Bayesian vector autoregression (BVAR) model.
Waggoner, Daniel F., Zha, Tao
core +6 more sources
Misspecification in mixed-model based association analysis [PDF]
Genetics, 2015 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.
Kruijer, Willem
core +4 more sources
The Learning Rate Is Not a Constant: Sandwich-Adjusted Markov Chain Monte Carlo Simulation [PDF]
EntropyA fundamental limitation of maximum likelihood and Bayesian methods under model misspecification is that the asymptotic covariance matrix of the pseudo-true parameter vector θ* is not the inverse of the Fisher information, but rather the sandwich ...
Jasper A. Vrugt, Cees G. H. Diks
doaj +2 more sources
Structured Ambiguity and Model Misspecification
SSRN Electronic Journal, 2019 An ambiguity averse decision maker evaluates plans under a restricted family of what we call structured models and unstructured alternatives that are statistically close to them.
L. Hansen, T. Sargent
semanticscholar +2 more sources
Learning Robust Statistics for Simulation-based Inference under Model Misspecification [PDF]
Neural Information Processing Systems, 2023 Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models. However, such methods
Daolang Huang +4 more
semanticscholar +1 more source