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1. ZAP: Z -Value Adaptive Procedures for False Discovery Rate Control with Side Information

   https://doi.org/10.1111/rssb.12557  

   Leung, Dennis; Sun, Wenguang (November 2022, Journal of the Royal Statistical Society Series B: Statistical Methodology)

   Abstract Adaptive multiple testing with covariates is an important research direction that has gained major attention in recent years. It has been widely recognised that leveraging side information provided by auxiliary covariates can improve the power of false discovery rate (FDR) procedures. Currently, most such procedures are devised with p-values as their main statistics. However, for two-sided hypotheses, the usual data processing step that transforms the primary statistics, known as p-values, into p-values not only leads to a loss of information carried by the main statistics, but can also undermine the ability of the covariates to assist with the FDR inference. We develop a p-value based covariate-adaptive (ZAP) methodology that operates on the intact structural information encoded jointly by the p-values and covariates. It seeks to emulate the oracle p-value procedure via a working model, and its rejection regions significantly depart from those of the p-value adaptive testing approaches. The key strength of ZAP is that the FDR control is guaranteed with minimal assumptions, even when the working model is misspecified. We demonstrate the state-of-the-art performance of ZAP using both simulated and real data, which shows that the efficiency gain can be substantial in comparison with p-value-based methods. Our methodology is implemented in the R package zap. 

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2. The complete chloroplast genome of the threatened Napa False Indigo Amorpha californica var. napensis Jeps. 1925 (Fabaceae) from Northern California, USA

   https://doi.org/10.1080/23802359.2022.2029605  

   Agudelo, Ivan D.; Aldaco, Griselda; Brito-Pizano, Angel; Chavez, Kimberly G.; Cortina, Karina G.; Flores, Jorge; Fuentes, Alejandro; Garcia, Adam N.; Garcia, Alejandro; Gonzalez-Martinez, Daniel; et al (January 2022, Mitochondrial DNA Part B)
3. Online control of the false coverage rate and false sign rate

   Weinstein, Asaf; Ramdas, Aaditya (July 2020, Proceedings of Machine Learning Research)

   The reproducibility debate has caused a renewed interest in changing how one reports uncertainty, from 𝑝-value for testing a null hypothesis to a confidence interval (CI) for the corresponding parameter. When CIs for multiple selected parameters are being reported, the analog of the false discovery rate (FDR) is the false coverage rate (FCR), which is the expected ratio of number of reported CIs failing to cover their respective parameters to the total number of reported CIs. Here, we consider the general problem of FCR control in the online setting, where one encounters an infinite sequence of fixed unknown parameters ordered by time. We propose a novel solution to the problem which only requires the scientist to be able to construct marginal CIs. As special cases, our framework yields algorithms for online FDR control and online sign-classification procedures that control the false sign rate (FSR). All of our methodology applies equally well to prediction intervals, having particular implications for selective conformal inference. 

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4. Semi-inclusive jet functions and jet substructure in $J_{E_T}^{\left(I\right)}$ and $J_{E_T}^{\left(II\right)}$ algorithms

   https://doi.org/10.1103/PhysRevD.103.054043  

   Wang, Lei; Kang, Zhong-Bo; Xing, Hongxi; Zhang, Ben-Wei (March 2021, Physical Review D)

   null (Ed.)
5. Potential pitfalls of false positives

   https://doi.org/10.1007/978-3-031-64315-6_45  

   Dey, I; Gnesdilow, D; Passonneau, R; Puntambekar, S (July 2024, International Conference on Artificial Intelligence in Education 2024 Proceedings)

   Automated writing evaluation (AWE) systems automatically assess and provide students with feedback on their writing. Despite learning benefits, students may not effectively interpret and utilize AI-generated feedback, thereby not maximizing their learning outcomes. A closely related issue is the accuracy of the systems, that students may not understand, are not perfect. Our study investigates whether students differentially addressed false positive and false negative AI-generated feedback errors on their science essays. We found that students addressed nearly all the false negative feedback; however, they addressed less than one-fourth of the false positive feedback. The odds of addressing a false positive feedback was 99% lower than addressing a false negative feedback, representing significant missed opportunities for revision and learning. We discuss the implications of these findings in the context of students’ learning. 

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