Results 11 to 20 of about 17,609 (115)
Contemporary Clinical Trials Communications, 2021 Covariate constrained randomization (CCR) is a method of controlling imbalance in important baseline covariates in cluster-randomized trials (CRT). We use simulated CRTs to investigate the performance (control of imbalance) of CCR relative to simple ...
Madeleine Organ +5 more
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A critical re-evaluation of the regression model specification in the US D1 EQ-5D value function
Population Health Metrics, 2012 Background The EQ-5D is a generic health-related quality of life instrument (five dimensions with three levels, 243 health states), used extensively in cost-utility/cost-effectiveness analyses.
Rand-Hendriksen Kim +2 more
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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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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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Interpretation and Semiparametric Efficiency in Quantile Regression under Misspecification
Econometrics, 2015 Allowing for misspecification in the linear conditional quantile function, this paper provides a new interpretation and the semiparametric efficiency bound for the quantile regression parameter β (
Ying-Ying Lee
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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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An Information Criterion for Auxiliary Variable Selection in Incomplete Data Analysis
Entropy, 2019 Statistical inference is considered for variables of interest, called primary variables, when auxiliary variables are observed along with the primary variables.
Shinpei Imori, Hidetoshi Shimodaira
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Robustness of Regularized Regression Methods Under Compound Model Misspecification: A Simulation Benchmarking Study [PDF]
Tutorials in Quantitative Methods for PsychologyRegularized regressions are widely used in psychological research where the fitted model is assumed to be correctly specified. Yet, psychological data routinely violates assumptions of linearity, homoscedasticity and additivity which raises questions ...
Ramazan, Onur, Lui, Yiu Wa
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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
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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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