Results 21 to 30 of about 15,773 (265)
Likelihood-based estimation and prediction for a measles outbreak in Samoa
Infectious Disease Modelling, 2023 Prediction of the progression of an infectious disease outbreak is important for planning and coordinating a response. Differential equations are often used to model an epidemic outbreak's behaviour but are challenging to parameterise. Furthermore, these
David Wu +4 more
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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 in a neighborhood of the reference model, based on one‐step adjustments.
Weidner, Martin, Bonhomme, Stéphane
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Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data
Econometrics, 2019 Researchers are often faced with the challenge of developing statistical models with incomplete data. Exacerbating this situation is the possibility that either the researcher’s complete-data model or the model of the missing-data mechanism is ...
Richard M. Golden +3 more
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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
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EconometricaEmpirical research typically involves a robustness‐efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more robust, but variable, unrestricted estimator.
Armstrong, Timothy B. +2 more
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Decision-Making Under Model Misspecification: DRO with Robust Bayesian Ambiguity Sets
EntropyDistributionally Robust Optimisation (DRO) protects risk-averse decision-makers by considering the worst-case risk within an ambiguity set of distributions based on the empirical distribution or a model. To further guard against finite, noisy data, model-
Charita Dellaporta +2 more
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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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Dynamic Concern for Misspecification [PDF]
Proceedings of the 24th ACM Conference on Economics and Computation, 2023 I consider an agent who posits a set of probabilistic models for the payoff‐relevant outcomes. The agent has a prior over this set but fears the actual model is omitted and hedges against this possibility. The concern for misspecification is endogenous: If a model explains the previous observations well, the concern attenuates.
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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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Model‐Driven Optimization of Subcutaneous Polymer Prodrugs Achieves Cancer Remission in Mice
Advanced Healthcare Materials, EarlyView.A pharmacokinetics/pharmacodynamics (PK/PD) model was developed to evaluate multiple dosing regimens for subcutaneously administered water‐soluble polymer prodrug for cancer therapy. The model enabled prediction of in vivo performance and contributed to the optimization of anticancer efficacy.
Anne Rodallec +5 more
wiley +1 more source