Simultaneous Statistical Inference

By Thorsten Dickhaus. Springer Berlin Heidelberg, 2014

1,023 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/451c2b09f99f7f99046863e8df49627776838e54

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Citations in the corpus

The Role of Vegetation in Mitigating Urban Land Surface Temperatures: A Case Study of Munich, Germany during the Warm Season (2015, methodology): “Furthermore, a single-step multiple comparison procedure and statistical test, the TukeyHSD (Honestly Significantly Difference), was computed to create a set of confidence intervals on the differences between the means of levels of a factor with the specified family-wise probability of coverage [30]”
CheXclusion: Fairness gaps in deep chest X-ray classifiers (2020, methodology): “05 significance level ( p ) are tested, we apply the Bonferroni correction [28]”
High-Content Screening for Quantitative Cell Biology. (2016, background): “Such approaches increase the ability to discover novel phenotypes, but often suffer from what is known as the ‘ multiple comparisons problem ’ , occurring when a large set of statistical inferences are considered simultaneously, leading to errors in inference [37].”
Distribution-free Multiple Testing (2016, background): “Multiple testing arises in a wide array of applied settings, ranging from anomaly detection in sensor arrays to the selection of genes that are differentially expressed [8, 10].”
From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “Benjamin and Hochberg) that are under strict assumptions [70–75].”
Statistical analysis of fNIRS data: A comprehensive review (2014, neutral): “In addition to Bonferroni correction , there are many variations of controlling FWER in multiple comparison problems, which include Least Significant Differences (LSD), Tukey, Scheffe, random field correction, etc. (Miller, 1981).”
Design and Analysis of Experiments (2001, neutral): “Montgomery (2011). “Designing Fractional Factorial Split-Plot Experiments using Integer Programming.” International Journal of Experimental Design and Process Optimisation, Vol. 2, pp. 34–47. Carmer, S. G., and M. R. Swanson (1973). “Evaluation of Ten Pairwise Multiple Comparison Procedures by Monte”
MacroBase: Analytic Monitoring for the Internet of Things (2016, background): “As we show, this ability to quickly process large data volumes can also improve result quality by combating bias due to the multiple testing problem [72], thereby improving statistical significance of results.”
A new criterion for assessing discriminant validity in variance-based structural equation modeling (2015, methodology): “” In real research situations with multiple constructs, the HTMTinference analysis involves the multiple testing problem (Miller 1981).”
LOPSIDED REASONING ON LOPSIDED TESTS AND MULTIPLE COMPARISONS (2012, methodology): “Quantitative trait loci analysis using the false discovery rate.”
On Ensuring that Intelligent Machines Are Well-Behaved (2017, methodology): “This is necessary to avoid the multiple comparisons problem [Miller, 2012] when computing the high confidence bounds on the discriminatory statistic using Hoeffding’s inequality.”