Results 41 to 50 of about 23,103 (324)
Bootstrap confidence sets under model misspecification [PDF]
, 2014 A multiplier bootstrap procedure for construction of likelihood-based confidence sets is considered for finite samples and a possible model misspecification.
Zhilova, Mayya, Spokoiny, Vladimir
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Journal of Agricultural and Resource Economics, 1994 This study presents an empirical method of modeling the nonnegativity of dependent variables using truncated logistic and normal disturbance distributions. The method is applied in estimating a ranch land hedonic price function.
Feng Xu +2 more
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On model selection and model misspecification in causal inference [PDF]
Statistical Methods in Medical Research, 2010 Standard variable selection procedures, primarily developed for the construction of outcome prediction models, are routinely applied when assessing exposure effects in observational studies. We argue that this tradition is sub-optimal and prone to yield bias in exposure effect estimators as well as their corresponding uncertainty estimators.
Vansteelandt, Stijn +2 more
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The Impact of Model-Misspecification on Model Based Personalised Dosing [PDF]
, 2016 Model Based Personalised Dosing (MBPD) requires a population pharmacokinetic (PK) or pharmacodynamic model to determine the optimal dose of medication for an individual.
Playford, EG +7 more
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Stats, 2022 Structural equation models (SEM), or confirmatory factor analysis as a special case, contain model parameters at the measurement part and the structural part.
Alexander Robitzsch
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Specifying Turning Point in Piecewise Growth Curve Models: Challenges and Solutions
Frontiers in Applied Mathematics and Statistics, 2017 Piecewise growth curve model (PGCM) is often used when the underlying growth process is not linear and is hypothesized to consist of phasic developments connected by turning points (or knots or change points).
Ling Ning, Wen Luo
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Informational herding with model misspecification [PDF]
Journal of Economic Theory, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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
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Market selection and learning under model misspecification [PDF]
, 2023 This paper studies market selection in an Arrow-Debreu economy with complete markets where agents learn over misspecified models. Under model misspecification, standard Bayesian learning loses its formal justification and biased learning processes may ...
Bottazzi, Giulio +2 more
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Robust Online Control with Model Misspecification
CoRR, 2021 We study online control of an unknown nonlinear dynamical system that is approximated by a time-invariant linear system with model misspecification. Our study focuses on robustness, a measure of how much deviation from the assumed linear approximation can be tolerated by a controller while maintaining finite $\ell_2$-gain.
Xinyi Chen 0001 +3 more
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