Results 41 to 50 of about 21,776,804 (321)
Informational herding with model misspecification [PDF]
Journal of Economic Theory, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
On the pitfalls of Gaussian likelihood scoring for causal discovery
Journal of Causal Inference, 2023 We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising
Schultheiss Christoph, Bühlmann Peter
doaj +1 more source
Monetary Policy Misspecification in VAR Models [PDF]
SSRN Electronic Journal, 2000 We examine the effects of extracting monetary policy disturbances with semi-structural and structural VARs, using data generated by a limited participation model under partial accommodative and feedback rules. We find that, in general, misspecification is substantial: short run coefficients often have wrong signs; impulse responses and variance ...
Fabio Canova, Joaquim Pires Pina
openaire +4 more sources
Bias of the Quasi Score Estimator of a Measurement Error Model Under Misspecification of the Regressor Distribution [PDF]
, 2003 In a structural error model the structural quasi score (SQS) estimator is based on the distribution of the latent regressor variable. If this distribution is misspecified the SQS estimator is (asymptotically) biased.
Cheng, Chi-Lun, Schneeweiß, Hans
core +2 more sources
Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data [PDF]
Biometrika, 2017 Propensity scores are widely used with inverse probability weighting to estimate treatment effects in observational studies. We study calibrated estimation as an alternative to maximum likelihood estimation for fitting logistic propensity score models.
Z. Tan
semanticscholar +1 more source
Bayesian graph selection consistency under model misspecification
Bernoulli, 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, Yabo +2 more
openaire +5 more sources
BMC Genetics, 2006 Background Studies of model-based linkage analysis show that trait or marker model misspecification leads to decreasing power or increasing Type I error rate.
Wilson Alexander F +4 more
doaj +1 more source
Frontiers in EducationA simulation study is designed to explore the accuracy of attribute parameter estimation in the crossed random effects linear logistic test model (CRELLTM) with the impact of Q-matrix misspecification on attribute parameter estimation using the SAS ...
Yi-Hsin Chen +3 more
doaj +1 more source
Characterization of the asymptotic distribution of semiparametric M-estimators [PDF]
, 2010 This paper develops a concrete formula for the asymptotic distribution of two-step, possibly non-smooth semiparametric M-estimators under general misspecification.
Ichimura, H, Lee, S
core +3 more sources
Target Matrix Estimators in Risk-Based Portfolios
Risks, 2018 Portfolio weights solely based on risk avoid estimation errors from the sample mean, but they are still affected from the misspecification in the sample covariance matrix.
Marco Neffelli
doaj +1 more source