References
- Barry VW, McClain AC, Shuger S, et al. Using a technology-based intervention to promote weight loss in sedentary overweight or obese adults: a randomized controlled trial study design. Diabetes Metab Syndr Obes. 2011;4:67–77. doi: 10.2147/DMSO.S14526
- Qin G, Zhang J, Zhu Z. Simultaneous mean and covariance estimation of partially linear models for longitudinal data with missing responses and covariate measurement error. Comput Stat Data Anal. 2016;96:24–39. doi: 10.1016/j.csda.2015.11.001
- Mallows CL. Some comments on c p. Technometrics. 1973;15(4):661–675.
- Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr. 1974;19(6):716–723. doi: 10.1109/TAC.1974.1100705
- Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–464. doi: 10.1214/aos/1176344136
- Breiman L. Heuristics of instability and stabilization in model selection. Ann Stat. 1996;24(6):2350–2383. doi: 10.1214/aos/1032181158
- Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B (Methodological). 1996;58:267–288.
- Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc. 2001;96(456):1348–1360. doi: 10.1198/016214501753382273
- Zhang CH. Nearly unbiased variable selection under minimax concave penalty. Ann Stat. 2010;38:894–942. doi: 10.1214/09-AOS729
- Kim Y, Choi H, Oh HS. Smoothly clipped absolute deviation on high dimensions. J Am Stat Assoc. 2008;103(484):1665–1673. doi: 10.1198/016214508000001066
- Fan J, Lv J. A selective overview of variable selection in high dimensional feature space. Stat Sin. 2010;20(1):101.
- Wasserman L, Roeder K. High dimensional variable selection. Ann Stat. 2009;37(5A):2178–2201. doi: 10.1214/08-AOS646
- Wei F, Huang J, Li H. Variable selection and estimation in high-dimensional varying-coefficient models. Stat Sin. 2011;21(4):1515–1540.
- Wang L, Zhou J, Qu A. Penalized generalized estimating equations for high-dimensional longitudinal data analysis. Biometrics. 2012;68(2):353–360. doi: 10.1111/j.1541-0420.2011.01678.x
- Rubin DB. Inference and missing data. Biometrika. 1976;63(3):581–592. doi: 10.1093/biomet/63.3.581
- Robins JM, Rotnitzky A. Semiparametric efficiency in multivariate regression models with missing data. J Am Stat Assoc. 1995;90(429):122–129. doi: 10.1080/01621459.1995.10476494
- Paik MC. The generalized estimating equation approach when data are not missing completely at random. J Am Stat Assoc. 1997;92(440):1320–1329. doi: 10.1080/01621459.1997.10473653
- Bang H, Robins JM. Doubly robust estimation in missing data and causal inference models. Biometrics. 2005;61(4):962–973. doi: 10.1111/j.1541-0420.2005.00377.x
- Kang JD, Schafer JL. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Stat Sci. 2007;22:523–539. doi: 10.1214/07-STS227
- Qu A, Lindsay BG, Lu L. Highly efficient aggregate unbiased estimating functions approach for correlated data with missing at random. J Am Stat Assoc. 2010;105(489):194–204. doi: 10.1198/jasa.2009.tm08506
- Garcia RI, Ibrahim JG, Zhu H. Variable selection for regression models with missing data. Stat Sin. 2010;20(1):149–165.
- Breheny P, Huang J. Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann Appl Stat. 2011;5(1):232–253. doi: 10.1214/10-AOAS388
- Zou H, Li R. One-step sparse estimates in nonconcave penalized likelihood models. Ann Stat. 2008;36(4):1509–1533. doi: 10.1214/009053607000000802
- Zhang CH, Huang J. The sparsity and bias of the lasso selection in high-dimensional linear regression. Ann Stat. 2008;36:1567–1594. doi: 10.1214/07-AOS520
- Shuger SL, Barry VW, Sui X, et al. Electronic feedback in a diet-and physical activity-based lifestyle intervention for weight loss: a randomized controlled trial. Int J Behav Nutr Phys Act. 2011;8(41):1–8.