R package FAMT: Factor Analysis for Multiple Testing (FAMT) : simultaneous tests under dependence in high-dimensional data. The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.

References in zbMATH (referenced in 32 articles , 2 standard articles )

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  1. Carpentier, Alexandra; Delattre, Sylvain; Roquain, Etienne; Verzelen, Nicolas: Estimating minimum effect with outlier selection (2021)
  2. Fan, Jianqing; Wang, Kaizheng; Zhong, Yiqiao; Zhu, Ziwei: Robust high-dimensional factor models with applications to statistical machine learning (2021)
  3. Gerard, David; Stephens, Matthew: Unifying and generalizing methods for removing unwanted variation based on negative controls (2021)
  4. Chen, Xi; Zhou, Wen-Xin: Robust inference via multiplier bootstrap (2020)
  5. Devijver, Emilie; Perthame, Emeline: Prediction regions through inverse regression (2020)
  6. Yu, Chang; Zelterman, Daniel: Distributions associated with simultaneous multiple hypothesis testing (2020)
  7. Yu, Weichang; Ormerod, John T.; Stewart, Michael: Variational discriminant analysis with variable selection (2020)
  8. Fan, Jianqing; Ke, Yuan; Sun, Qiang; Zhou, Wen-Xin: Farmtest: factor-adjusted robust multiple testing with approximate false discovery control (2019)
  9. Jeng, X. Jessie; Chen, Xiongzhi: Predictor ranking and false discovery proportion control in high-dimensional regression (2019)
  10. Bodwin, Kelly; Zhang, Kai; Nobel, Andrew: A testing based approach to the discovery of differentially correlated variable sets (2018)
  11. Deng, Lu; Zi, Xuemin; Li, Zhonghua: False discovery rates for large-scale model checking under certain dependence (2018)
  12. Zhou, Wen-Xin; Bose, Koushiki; Fan, Jianqing; Liu, Han: A new perspective on robust (M)-estimation: finite sample theory and applications to dependence-adjusted multiple testing (2018)
  13. Zhao, Haibing: Estimating false discovery proportion in multiple comparison under dependency (2017)
  14. Blum, Yuna; Houée-Bigot, Magalie; Causeur, David: Sparse factor model for co-expression networks with an application using prior biological knowledge (2016)
  15. Bodnar, Taras; Reiß, Markus: Exact and asymptotic tests on a factor model in low and large dimensions with applications (2016)
  16. Delattre, Sylvain; Roquain, Etienne: On empirical distribution function of high-dimensional Gaussian vector components with an application to multiple testing (2016)
  17. Fan, Jianqing; Liao, Yuan; Wang, Weichen: Projected principal component analysis in factor models (2016)
  18. Jessie Jeng, X.: Detecting weak signals in high dimensions (2016)
  19. Perthame, Émeline; Friguet, Chloé; Causeur, David: Stability of feature selection in classification issues for high-dimensional correlated data (2016)
  20. Sheu, Ching-Fan; Perthame, Émeline; Lee, Yuh-Shiow; Causeur, David: Accounting for time dependence in large-scale multiple testing of event-related potential data (2016)

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