[1]
|
papageorgiou, g., bouboulis, p. and theodoridis, s. (2015) robust linear regression analysis—a greedy approach. ieee transactions on signal processing, 63, 3872-3887.
|
[2]
|
huber, p. (1972) the 1972 wald lecture robust statistics: a review. annals of mathematical statistics, 43, 1041-1067.
|
[3]
|
rousseeuw, p. and leroy, a. (1987) robust regression and outlier detection. wiley, new york, ny.
|
[4]
|
maronna, r., martin, r. and yohai, v. (2006) robust statistics: theory and methods. wiley, new york, ny.
|
[5]
|
huber, p. (1981) robust statistics. wiley, new york, ny.
|
[6]
|
cook, r. and weisberg, s. (1982) residuals and influence in regression. chapman and hall, new york, ny.
|
[7]
|
natarajan, b. (1995) sparse approximate solutions to linear systems. siam journal on computing, 24, 227-234.
|
[8]
|
nguyen, n. and tran, t. (2013) robust lasso with missing and grossly corrupted observa- tions. ieee transactions on information theory, 59, 2036-2058.
|
[9]
|
chen, j. and liu, y. (2019) stable recovery of structured signals from corrupted subgaussian measurements. ieee transactions on information theory, 65, 2976- 2994.
|
[10]
|
katayama, s. and fujisawa, h. (2017) sparse and robust linear regression: an optimization algorithm and its statistical properties. statistica sinica, 27, 1243-1264.
|
[11]
|
fan, j. and li, y. (2001) variable selection via nonconcave penalized likelihood and its oracle properties. journal of the american statistical association, 96, 1348-1360.
|
[12]
|
ong, c. and an, l. (2013) learning sparse classifiers with difference of convex functions algorithms. optimization methods and software, 28, 830-854.
|
[13]
|
peleg, d. and meir, r. (2008) a bilinear formulation for vector sparsity optimization. signal processing, 88, 375-389.
|
[14]
|
zhang, c. (2010) nearly unbiased variable selection under minimax concave penalty. annals of statistics, 38, 894-942.
|
[15]
|
zhang, t. (2010) analysis of multi-stage convex relaxation for sparse regularization. journal of machine learning research, 11, 1081-1107.
|
[16]
|
cand´es, e., romberg, j. and tao, t. (2006) robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. ieee transactions on infor- mation theory, 52, 489-509.
|
[17]
|
huber, p. (1964) robust estimation of a location parameter. annals of mathematical statis- tics, 35, 73-101.
|
[18]
|
fan, j., li, q. and wang, y. (2017) estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions. journal of royal statistical society, series b, 79, 247-265.
|
[19]
|
yi, c. and huang, j. (2017) semismooth newton coordinate descent algorithm for elastic- net penalized huber loss regression and quantile regression. journal of computational and graphical statistics, 26, 547-557.
|
[20]
|
[20] sun, q., zhou, w. and fan, j. (2020) adaptive huber regression. journal of the american statistical association, 115, 254-265.
|
[21]
|
peng, d. and chen, x. (2020) computation of second-order directional stationary points for group sparse optimization. optimization methods and software, 35, 348-376.
|
[22]
|
zhang, x. and peng, d. (2022) solving constrained nonsmooth group sparse optimization via group capped-l1 relaxation and group smoothing proximal gradient algorithm. com- putational optimization and applications, 83, 801-844.
|
[23]
|
彭定涛, 唐琦, 张弦. 组稀疏优化问题精确连续capped-l1松弛[j]. 数学学报, 2022, 65(2): 243-262.
|
[24]
|
罗孝敏, 彭定涛, 张弦. 基于mcp正则的最小一乘回归问题研究[j]. 系统科学与数学, 2021, 41(8): 2327-2337.
|
[25]
|
ahn, m., pang, j. and jack, x. (2017) difference-of-convex learning: directional stationarity, optimality, and sparsity. siam journal on optimization, 27, 1637-1665.
|
[26]
|
chen, x., niu, l. and yuan, y. (2013) optimality conditions and a smoothing trust region newton method for non-lipschitz optimization. siam journal on optimization, 23, 1528- 1552.
|