Testing high-dimensional mediation effect with arbitrary exposure–mediator coefficients
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DOI: 10.1007/s11749-025-00971-z
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- Zhonghua Liu & Jincheng Shen & Richard Barfield & Joel Schwartz & Andrea A. Baccarelli & Xihong Lin, 2022. "Large-Scale Hypothesis Testing for Causal Mediation Effects with Applications in Genome-wide Epigenetic Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(537), pages 67-81, January.
- Lotte C. Houtepen & Christiaan H. Vinkers & Tania Carrillo-Roa & Marieke Hiemstra & Pol A. van Lier & Wim Meeus & Susan Branje & Christine M. Heim & Charles B. Nemeroff & Jonathan Mill & Leonard C. Sc, 2016. "Genome-wide DNA methylation levels and altered cortisol stress reactivity following childhood trauma in humans," Nature Communications, Nature, vol. 7(1), pages 1-10, April.
- James Y. Dai & Janet L. Stanford & Michael LeBlanc, 2022. "A Multiple-Testing Procedure for High-Dimensional Mediation Hypotheses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(537), pages 198-213, January.
- Ruixuan Rachel Zhou & Liewei Wang & Sihai Dave Zhao, 2020. "Estimation and inference for the indirect effect in high-dimensional linear mediation models," Biometrika, Biometrika Trust, vol. 107(3), pages 573-589.
- Yen-Tsung Huang & Wen-Chi Pan, 2016. "Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators," Biometrics, The International Biometric Society, vol. 72(2), pages 402-413, June.
- Xu Guo & Runze Li & Jingyuan Liu & Mudong Zeng, 2022. "High-Dimensional Mediation Analysis for Selecting DNA Methylation Loci Mediating Childhood Trauma and Cortisol Stress Reactivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1110-1121, September.
- Tingni Sun & Cun-Hui Zhang, 2012. "Scaled sparse linear regression," Biometrika, Biometrika Trust, vol. 99(4), pages 879-898.
- Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.
- Cun-Hui Zhang & Stephanie S. Zhang, 2014. "Confidence intervals for low dimensional parameters in high dimensional linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 217-242, January.
- Tianxi Cai & T. Tony Cai & Zijian Guo, 2021. "Optimal statistical inference for individualized treatment effects in high‐dimensional models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 669-719, September.
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