selective inference
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2022 ◽  
Vol 167 ◽  
pp. 107350
David Rügamer ◽  
Philipp F.M. Baumann ◽  
Sonja Greven

Arun K. Kuchibhotla ◽  
John E. Kolassa ◽  
Todd A. Kuffner

We discuss inference after data exploration, with a particular focus on inference after model or variable selection. We review three popular approaches to this problem: sample splitting, simultaneous inference, and conditional selective inference. We explain how each approach works and highlight its advantages and disadvantages. We also provide an illustration of these post-selection inference approaches. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see for revised estimates.

2021 ◽  
Vol 14 (0) ◽  
pp. 1-11
Shinya Suzumura ◽  
Kazuya Nakagawa ◽  
Yuta Umezu ◽  
Koji Tsuda ◽  
Ichiro Takeuchi

2021 ◽  
Vol 15 (1) ◽  
Chihiro Watanabe ◽  
Taiji Suzuki

2020 ◽  
Vol 117 (26) ◽  
pp. 15028-15035 ◽  
Ronald Yurko ◽  
Max G’Sell ◽  
Kathryn Roeder ◽  
Bernie Devlin

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptiveP-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS associationPvalues play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene–gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.

Kosuke Tanizaki ◽  
Noriaki Hashimoto ◽  
Yu Inatsu ◽  
Hidekata Hontani ◽  
Ichiro Takeuchi

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