References
- Li T, Levina E, Zhu J, et al. Prediction models for network-linked data. Ann Appl Stat. 2019;13(1):132–164.
- Harris KM, Udry JR. The national longitudinal study of adolescent to adult health (add health), Waves I & II, 1994–1996; Wave III, 2001–2002; Wave IV, 2007–2009 [machine-readable data file and documentation]. Chapel Hill (NC): Carolina Population Center, University of North Carolina at Chapel Hill 10; 2009.
- Almutairi KM, Alonazi WB, Vinluan JM, et al. Health promoting lifestyle of university students in Saudi Arabia: a cross-sectional assessment. BMC Public Health. 2018;18(1):1093.
- Wang W, Neuman E, Newman D. Statistical power of the social network autocorrelation model. Soc Netw. 2014;38(1):88–99.
- Zhou J, Tu Y, Chen Y, et al. Estimating spatial autocorrelation with sampled network data.J Bus Econ Stat. 2017;35(1):130–138.
- Su L, Lu W, Song R, et al. Testing and estimation of social network dependence with time to event data. J Am Stat Assoc. 2020;115(530):570–582.
- Ma S, Huang J. A concave pairwise fusion approach to subgroup analysis. J Am Stat Assoc. 2017;112(517):410–423.
- Li C, Li H. Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics. 2008;24(9):1175–1182.
- Dirmeier S, Fuchs C, Mueller NS, et al. netReg: network-regularized linear models for biological association studies. Bioinformatics. 2017;34(5):896–898.
- Ren J, Du Y, Li S, et al. Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis. Genet Epidemiol. 2019;43(3):276–291.
- Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B. 1994;58(1):267–288.
- Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties.J Am Stat Assoc. 2001;96(456):1348–1360.
- Zhang C. Nearly unbiased variable selection under minimax concave penalty. Ann Statist. 2010;38(2):894–942.
- Wen C, Wang X, Wang S. Laplace error penalty based variable selection in ultra-high dimension. Scand J Statist. 2015;42(3):685–700.
- Huang J, Jiao Y, Liu Y, et al. A constructive approach to L0 penalized regression. J Mach Learn Res. 2018;19(1):1–37.
- Bertsimas D, King A, Mazumder R. Best subset selection via a modern optimization lens. Ann Statist. 2016;44(2):813–852.
- Mazumder R, Radchenko P, Dedieu A. Subset selection with shrinkage: sparse linear modeling when the SNR is low. 2017. arxiv:1708.03288.
- Hazimeh H, Mazumder R. Fast best subset selection: coordinate descent and local combinatorial optimization algorithms. Oper Res. 2018;68(5):1517–1537.
- Allman ES, Matias C, Rhodes JA. Identifiability of parameters in latent structure models with many observed variables. Ann Statist. 2009;37(6):3099–3132.
- Xie T, Cao R, Du J. Variable selection for spatial autoregressive models with a diverging number of parameters. Statist Papers. 2020;61(1–3):1125–1145.
- Cai L, Maiti T, Bo L. Variable selection and estimation for high dimensional spatial autoregressive models. Scand J Statist. 2020;47(2):587–607.
- Wen C, Zhang A, Quan S, et al. BeSS: an R package for best subset selection in linear, logistic and CoxPH models. J Stat Softw. 2020;94(4):1–24.