一种基于错误发现率的模型选择规则

doi:10.16088/j.issn.1001-6600.2025030703

Abstract

Abstract: For high-dimensional sparse linear regression models, this paper proposed an FDR rule for model selection based on false discovery rate (FDR) from the perspective of posterior estimation, and then introduced a dynamic signal-to-noise ratio (SNR) change factor on this basis. The FDR_R rule, which was more robust to SNR variations and was invariant to data scale, was proposed. Combined with the OMP algorithm, simulation experiments compared the probabilities of successfully selecting all true variables and the FDR values for the FDR rule, FDR_R rule, and existing rules. The results show that the FDR_R rule is more robust than the other rules in high SNR or large sample sizes, more resistant to data scaling issues, and achieves the lowest FDR. Finally, the proposed method was applied to real data from patients with mantle cell lymphoma, identifying genes associated with cell proliferation.

Key words: high-dimensional model selection, FDR, FDR_R, OMP algorithm, SNR

CLC Number: O212.8

RONG Jingjing, YE Jimin. A Model Selection Criterion Based on False Discovery Rate[J].Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(1): 110-118.

References

[1] DING J, TAROKH V, YANG Y H. Model selection techniques: an overview[J]. IEEE Signal Processing Magazine, 2018, 35(6): 16-34. DOI: 10.1109/MSP.2018.2867638.
[2] STOICA P, SELEN Y. Model-order selection: a review of information criterion rules[J]. IEEE Signal Processing Magazine, 2004, 21(4): 36-47. DOI: 10.1109/MSP.2004.1311138.
[3] BOGDAN M, FROMMLET F. Identifying important predictors in large data bases-multiple testing and model selection[M]. Handbook of Multiple Comparisons, Boca Raton, FL:Chapman and Hall/CRC, 2021: 139-182.
[4] MEIR E, ROUTTENBERG T. Cramér-Rao bound for estimation after model selection and its application to sparse vector estimation[J]. IEEE Transactions on Signal Processing, 2021, 69: 2284-2301. DOI: 10.1109/TSP.2021.3068356.
[5] AKAIKE H. A new look at the statistical model identification[J]. IEEE Transactions on Automatic Control, 1974, 19(6): 716-723. DOI: 10.1109/TAC.1974.1100705.
[6] SCHWARZ G. Estimating the dimension of a model[J].The Annals of Statistics, 1978, 6(2): 461-464.
[7] 王斐, 许波. 基于自适应LPP特征降维和改进VPMCD的滚动轴承故障诊断[J]. 现代制造工程, 2024(6): 154-161, 94. DOI: 10.16731/j.cnki.1671-3133.2024.06.020.
[8] 王逸林, 马世龙, 王晋晋, 等. 基于稀疏重构的色噪声背景下未知线谱信号估计[J]. 电子与信息学报, 2018, 40(11): 2570-2577. DOI: 10.11999/JEIT171040.
[9] ISHIJIMA R, EBIHARA T, WAKATSUKI N, et al. Sparse channel estimation with global optimum solution for orthogonal signal division multiplexing in underwater acoustic communication[J]. IEEE Access, 2024, 12: 128778-128790.
[10] TIBSHIRANI R. Regression shrinkage and selection via the Lasso[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 1996, 58(1): 267-288. DOI: 10.1111/j.2517-6161.1996.tb02080.x.
[11] ZOU H, HASTIE T. Regularization and variable selection via the elastic net[J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 2005, 67(2): 301-320. DOI: 10.1111/j.1467-9868.2005.00503.x.
[12] 姜云卢, 卢辉杰, 黄晓雯. 惩罚加权复合分位数回归方法在固定效应面板数据中的应用研究[J]. 广西师范大学学报(自然科学版), 2025,43(6): 120-127. DOI: 10.16088/j.issn.1001-6600.2024111001.
[13] CAI T T, WANG L. Orthogonal matching pursuit for sparse signal recovery with noise[J]. IEEE Transactions on Information Theory, 2011, 57(7): 4680-4688. DOI: 10.1109/TIT.2011.2146090.
[14] CHEN J H, CHEN Z H. Extended Bayesian information criteria for model selection with large model spaces[J].Biometrika, 2008, 95(3): 759-771. DOI: 10.1093/biomet/asn034.
[15] OWRANG A, JANSSON M. A model selection criterion for high-dimensional linear regression[J]. IEEE Transactions on Signal Processing, 2018, 66(13): 3436-3446. DOI: 10.1109/TSP.2018.2821628.
[16] BABU P, STOICA P. Multiple-hypothesis testing rules for high-dimensional model selection and sparse-parameter estimation[J]. Signal Processing, 2023, 213: 109189. DOI: 10.1016/j.sigpro.2023.109189.
[17] 徐萍, 钟思敏, 李斌斌, 等. 基于稀疏超高维非参数可加模型的条件独立筛选[J]. 广西师范大学学报(自然科学版), 2022, 40(1): 100-107. DOI: 10.16088/j.issn.1001-6600.2021060919.
[18] 潘莹丽, 刘展, 闫玲玲. 基于大规模高维线性回归模型的分布式计算方法研究[J]. 应用数学学报, 2022, 45(3):339-354. DOI: 10.3969/j.issn.1006-3110.2018.06.002.
[19] GOHAIN P B, JANSSON M. Robust information criterion for model selection in sparse high-dimensional linear regression models[J]. IEEE Transactions on Signal Processing, 2023, 71: 2251-2266. DOI: 10.1109/TSP.2023.3284365.
[20] STOICA P, BABU P. On the proper forms of BIC for model order selection[J]. IEEE Transactions on Signal Processing, 2012, 60(9): 4956-4961. DOI: 10.1109/TSP.2012.2203128.
[21] SCHMIDT D F, MAKALIC E. The consistency of MDL for linear regression models with increasing signal-to-noise ratio[J]. IEEE Transactions on Signal Processing, 2011, 60(3): 1508-1510. DOI: 10.1109/TSP.2011.2177833.
[22] STOICA P, BABU P. False discovery rate (FDR) and familywise error rate (FER) rules for model selection in signal processing applications[J]. IEEE Open Journal of Signal Processing, 2022, 3: 403-416.
[23] BENJAMINI Y, YEKUTIELI D. The control of the false discovery rate in multiple testing under dependency[J]. The Annals of Statistics, 2001, 29(4):1165-1188. DOI: 10.1214/aos/1013699998.
[24] BUNEA F, WEGKAMP M H, AUGUSTE A. Consistent variable selection in high dimensional regression via multiple testing[J]. Journal of Statistical Planning and Inference, 2006, 136(12): 4349-4364. DOI: 10.1016/j.jspi.2005.03.011.
[25] 邹航, 姜云卢. 高维线性回归模型稳健变量选择方法综述[J]. 应用概率统计, 2024, 40(1): 157-181. DOI: 10.3969/j.issn.1001-4268.2024.01.010.
[26] 黄河, 潘莹丽. Cox模型中基于Model-X Knockoffs的高维控制变量选择方法[J]. 统计与决策, 2023, 39(5): 16-21. DOI: 10.13546/j.cnki.tjyjc.2023.05.003.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	LIU Xiaojuan, LIN Lu, HU Yucong, PAN Lei. Research on the Influence of Land Use Types Surrounding Stations on Subway Passenger Satisfaction[J]. Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(6): 1 -12 .
[2]	HAN Huabin, GAO Bingpeng, CAI Xin, SUN Kai. Fault Diagnosis of Wind Turbine Blade Icing Based on HO-CNN-BiLSTM-Transformer Model[J]. Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(6): 13 -28 .
[3]	CHEN Jianguo, LIANG Enhua, SONG Xuewei, QIN Zhangrong. Lattice Boltzmann Simulation for the Aqueous Humour Dynamics of the Human Eye Based on 3D Reconstruction of OCT Images[J]. Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(6): 29 -41 .
[4]	LI Hao, HE Bing. Droplet Rebound Behavior on Grooves Surface[J]. Journal of Guangxi Normal University(Natural Science Edition), 2025, 43(6): 42 -53 .
[5]	TIAN Sheng, ZHAO Kailong, MIAO Jialin. Research on Automatic Driving Road Traffic Detection Algorithm Based on Improved YOLO11n Model[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(1): 1 -9 .
[6]	HUANG Yanguo, XIAO Jie, WU Shuiqing. Bidirectional Efficient Multi-scale Traffic Flow Prediction Based on D2STGNN[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(1): 10 -22 .
[7]	LIU Zhihao, LI Zili, SU Min. YOLOv8-based Helmet Detection Method for Electric Vehicle Riders Combining Intelligent Communication and UAV-Assistance[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(1): 23 -32 .
[8]	ZHANG Zhulu, LI Huaqiang, LIU Yang, XU Lixiong. Non-intrusive Load Identification Based on Bi-LSTM Feature Fusion and FT-FSL[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(1): 33 -44 .
[9]	WANG Tao, LI Yuansong, SHI Rui, CHEN Huining, HOU Xianqing. MGDE-UNet: Defect Segmentation Model for Lightweight Photovoltaic Cells[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(1): 45 -55 .
[10]	HUANG Wenjie, LUO Weiping, CHEN Zhennan, PENG Zhixiang, DING Zihao. Research on Lightweight PCB Defect Detection Algorithm Based on YOLO11[J]. Journal of Guangxi Normal University(Natural Science Edition), 2026, 44(1): 56 -67 .

A Model Selection Criterion Based on False Discovery Rate

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 1

Metrics

Comments

Recommended 10