Abstract This paper considers the problem of variable selection in high-dimensional longitudinal linear regression models with monotone missing patterns. A new variable selection procedure is proposed based on the smooth-threshold inverse probability weighted generalized estimating equation. The proposed procedure avoids the convex optimization problem without using penalty functions. Besides, the proposed method can automatically eliminate inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimate the nonzero regression coefficients. Under some regularity conditions, the variable selection procedure is proved to have Oracle property. Finally, some simulation studies are conducted to examine the finite sample property of the proposed variable selection procedure.
TANG Yang-bing, TIAN Rui-qin, XU Deng-ke. Variable selection for high-dimensional longitudinal linear regression models with monotone missing patterns. Applied Mathematics A Journal of Chinese Universities, 2017, 32(2): 241-252.
