Results 101 to 110 of about 13,855,951 (296)
Copula‐based joint modelling of emergency department visits with time‐varying dependence
Canadian Journal of Statistics, EarlyView.Abstract Jointly modelling multiple correlated count time series is essential in health services research, where outcomes like emergency visits for mental health and substance use often evolve together. Ignoring these dependencies can obscure meaningful trends and limit the effectiveness of policy evaluation.
Guanjie Lyu, Cindy Feng, Lihui Liu
wiley +1 more source
Generalized Bayes approach to inverse problems with model misspecification. [PDF]
Inverse Probl, 2023 Baek Y, Aquino W, Mukherjee S.
europepmc +1 more source
Assessing Estimation Uncertainty under Model Misspecification
, 2023 Model misspecification is ubiquitous in data analysis because the data-generating process is often complex and mathematically intractable. Therefore, assessing estimation uncertainty and conducting statistical inference under a possibly misspecified ...
Li, Rong, Li, Yang, Qin, Yichen
core
Canadian Journal of Statistics, EarlyView.Abstract This article presents a strategy for conducting regression analysis of zero‐truncated recurrent event data. The research is partly motivated by a pediatric mental health care (PMHC) program based on administrative data. We are particularly interested in how the occurrence of an event depends on its past occurrences and the associated ...
Anqi A. Chen +3 more
wiley +1 more source
A view on model misspecification in uncertainty quantification
, 2022 Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model
Loog, Marco +2 more
core
Vine copula knockoffs for variable selection in gene expression studies
Canadian Journal of Statistics, EarlyView.Abstract Identifying clinical and genetic markers is essential for stratifying cancer patients by survival outcomes and guiding personalized treatment strategies. However, gene expression studies often involve high‐dimensional predictors with mixed data types and complex dependence, which complicates reliable variable selection.
José Ulises Márquez Urbina +3 more
wiley +1 more source
Sparse maximum likelihood estimation of regression models
Canadian Journal of Statistics, EarlyView.Abstract For regression model selection and estimation, we study a small set of candidate models of maximum likelihood from which all information criteria such as the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) choose their models.
Min Tsao
wiley +1 more source
Mitigating measurement error in misspecified small area models
Canadian Journal of Statistics, EarlyView.Abstract In the framework of Small area estimation, we consider an area‐level model where a subset of covariates is measured with error. The extent of the error is assumed to be constant throughout the areas, and it is expressed by a scalar parameter γ$$ \gamma $$, which multiplies the deterministic covariance matrix of the estimator of the true ...
Diego Battagliese +3 more
wiley +1 more source
Modified interactive Q-learning for attenuating the impact of model misspecification with treatment effect heterogeneity. [PDF]
Stat Methods Med Res, 2023 Zhang Y, Vock DM, Patrick ME, Murray TA.
europepmc +1 more source
Canadian Journal of Statistics, EarlyView.Abstract Irregular observation times are common in longitudinal observational studies and can affect causal inferences. We use data from electronic health records of Kaiser Permanente Washington patients in the United States who initiated an antidepressant medication between 2008 and 2018, with a confirming diagnosis of depression. We are interested in
Janie Coulombe +2 more
wiley +1 more source