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   https://doi.org/10.1111/biom.13634  

   Zhang, Hong; Wu, Zheyang (February 2022, Biometrics)

   Abstract Combining dependent tests of significance has broad applications but the related p-value calculation is challenging. For Fisher's combination test, current p-value calculation methods (eg, Brown's approximation) tend to inflate the type I error rate when the desired significance level is substantially less than 0.05. The problem could lead to significant false discoveries in big data analyses. This paper provides two main contributions. First, it presents a general family of Fisher type statistics, referred to as the GFisher, which covers many classic statistics, such as Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, and so forth. The GFisher allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and the statistic-defining parameters to a given data. Second, the paper presents several new p-value calculation methods based on two novel ideas: moment-ratio matching and joint-distribution surrogating. Systematic simulations show that the new calculation methods are more accurate under multivariate Gaussian, and more robust under the generalized linear model and the multivariate t-distribution. The applications of the GFisher and the new p-value calculation methods are demonstrated by a gene-based single nucleotide polymorphism (SNP)-set association study. Relevant computation has been implemented to an R package GFisher available on the Comprehensive R Archive Network. 

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2. If I Value the Test Do I Feel More or Less Emotion? Exploration of Value and Emotions

   Husman, J.; Lampkins, S.; Villanueva, I.; Christensen, D.; Vicioso, P.; Youmans, K. (January 2018, International Conference on Motivation)
3. Revision of pyrophilous taxa of Pholiota described from North America reveals four species— P. brunnescens, P. castanea, P. highlandensis , and P. molesta

   https://doi.org/10.1080/00275514.2018.1516960  

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4. The correlation-bound anion of p -chloroaniline

   https://doi.org/10.1063/1.5096986  

   Ciborowski, Sandra M.; Harris, Rachel M.; Liu, Gaoxiang; Martinez-Martinez, Chalynette J.; Skurski, Piotr; Bowen, Jr., Kit H. (April 2019, The Journal of Chemical Physics)
5. Robust learning under clean-label attack

   Blum, A.; Hanneke, S.; Qian, J.; Shao, H. (January 2021, Conference on Learning Theory)

   Belkin, M.; Kpotufe, S. (Ed.)

   We study the problem of robust learning under clean-label data-poisoning attacks, where the at-tacker injects (an arbitrary set of) correctly-labeled examples to the training set to fool the algorithm into making mistakes on specific test instances at test time. The learning goal is to minimize the attackable rate (the probability mass of attackable test instances), which is more difficult than optimal PAC learning. As we show, any robust algorithm with diminishing attackable rate can achieve the optimal dependence on ε in its PAC sample complexity, i.e., O(1/ε). On the other hand, the attackable rate might be large even for some optimal PAC learners, e.g., SVM for linear classifiers. Furthermore, we show that the class of linear hypotheses is not robustly learnable when the data distribution has zero margin and is robustly learnable in the case of positive margin but requires sample complexity exponential in the dimension. For a general hypothesis class with bounded VC dimension, if the attacker is limited to add at most t >0 poison examples, the optimal robust learning sample complexity grows almost linearly with t. 

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