M-estimation is a widely used technique for robust statistical inference. In this paper, we study model selection and model averaging for M-estimation to simultaneously improve the coverage probability of confidence intervals of the parameters of interest and reduce the impact of heavy-tailed errors ...
Next, we focused on uncertainty estimation for the imputed accessibility values. We measured the uncertainty of the model for each imputed accessibility value by sampling from MultiVI’s generative model (Methods) and found a strong relationship between the estimated uncertainty and the error at each...
Model averaging has become a crucial statistical methodology, especially in situations where numerous models vie to elucidate a phenomenon. Over the past two decades, there has been substantial advancement in the theory of model averaging. However, a gap remains in the field regarding model averaging...
estimation methods in situations with the number of variables much smaller than the sample size, this article concentrates on the additional difficulties and challenges when applying focused model selection for squared error loss with penalized estimation, for example, in a context of high-dimensional ...
Hadley E, Rhea S, Jones K, Li L, Stoner M, Bobashev G. Enhancing the prediction of hospitalization from a COVID-19 agent-based model: a Bayesian method for model parameter estimation. PLoS One. 2022;17(3):e0264704. Article CAS PubMed PubMed Central Google Scholar Xiang HX, Fei J...
(2019). metaBMA: Bayesian model averaging for random and fixed effects meta-analysis. https://CRAN.R-project.org/package=metaBMA An (open-access) introduction to Bayesian meta-analysis with model averaging is available at: Gronau, Q. F., Heck, D. W., Berkhout, S. W., Haaf, J. M....
Traditional computational approaches to antibody binding affinity rely on estimation of free energy3,6,7. These are often limited by high computational cost, low throughput and the limited reliability of the free energy estimates generated by these methods7,8. Alternatively, machine learning techniques...
模型帮助模型平均modelforModel 系统标签: averagingjackknifemodelestimator重叠模型 JackknifeModelAveragingforQuantileRegressions∗XunLu andLiangjunSu DepartmentofEconomics,HongKongUniversityofScience&Technology SchoolofEconomics,SingaporeManagementUniversity,SingaporeJune12,2014AbstractInthispaperweconsidertheproblemoffrequenti...
The second step is to find suitable model weights for averaging. To minimize the prediction error, we estimate the model weights using a delete-one cross-validation procedure. Departing from the literature of model averaging that requires the weights always sum to one, an important improvement we...
adsorption capacity value to be higher than 2 m mol g−1as the threshold. The standard deviation consideration is to assure we account for statistical errors of adsorption predictions by our ensemble model. Categorization of the predicted high-performing MOFs by node-topology pair and ...