Cluster level inferencePermutation testingParametric testingFamily wise error ratesEuler characteristicsUPCROSSINGSTo understand better why parametric methods may yield excessive false positives, we assessed th
Due to weighting, some popular cluster-level summary approaches were found to lead to invalid inference in many settings. Finally, although use of bias-corrected empirical sandwich standard error estimates did not consistently result in nominal sizes, they did work well, thus supporting the ...
[inference about the] district location of the sampled clusters using a GIS software and the GPS dataset these data would not be statistically representative. However, I'm unclear how to interpret this advice. Is the warning given because the user is trying to draw inferences about the district...
177-190. doi:10.1016/j.jneumeth.2007.03.024 TFCE originally described in Smith/Nichols (2009), "Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence, and localisation in cluster inference", NeuroImage 44 (2009) 83-98. """ n_samples, n_times, n_vertices ...
Applied comparison of large-scale propensity score matching and cardinality matching for causal inference in observational research. BMC Med Res Methodol. 2021;21(1):109. Article CAS PubMed PubMed Central Google Scholar Dell-Kuster S, Droeser RA, Schäfer J, Gloy V, Ewald H, Schandelmaier...
Applied comparison of large-scale propensity score matching and cardinality matching for causal inference in observational research. BMC Med Res Methodol. 2021;21(1):109. Article CAS PubMed PubMed Central Google Scholar Dell-Kuster S, Droeser RA, Schäfer J, Gloy V, Ewald H, Schandelmaier...
Y Shin,JE Lafata,Y Cao - 《Journal of Statistical Planning & Inference》 被引量: 0发表: 2018年 Improving the Design of Cluster-Randomized Trials in Education: Informing the Selection of Variance Design Parameter Values for Science Achievement Studies The purpose of this three-essay dissertation ...
However, increased power with broader levels of inference may diminish the specificity to localize effects, especially for non-task contexts. These findings underscore the benefit of shifting the scale of inference to better capture the underlying signal, which may unlock opportunities for discovery in...
Nonparametric methods for statistical inference when conducting multiple statistical tests, in contrast, are thought to produce false positives at the nominal rate, which has thus led to the suggestion that previously reported studies should reanalyze their fMRI data using nonparametric tools....
Recently, there has been growing interest in using machine learning (ML) methods for causal inference due to their automatic and flexible abilities to model the propensity score and the outcome model. However, almost all the ML methods for causal inference have been studied under the assumption ...