Functional Linear Model with Zero-value Coefficient Function at Sub-regions.
We propose a shrinkage method to estimate the coefficient function in a functional linear regression model when the value of the coefficient function is zero within certain sub-regions. Besides identifying the null region in which the coefficient function is zero, we also aim to perform estimation and inferences for the nonparametrically estimated coefficient function without over-shrinking the values. Our proposal consists of two stages. In stage one, the Dantzig selector is employed to provide initial location of the null region. In stage two, we propose a group SCAD approach to refine the estimated location of the null region and to provide the estimation and inference procedures for the coefficient function. Our considerations have certain advantages in this functional setup. One goal is to reduce the number of parameters employed in the model. With a one-stage procedure, it is needed to use a large number of knots in order to precisely identify the zero-coefficient region; however, the variation and estimation difficulties increase with the number of parameters. Owing to the additional refinement stage, we avoid this necessity and our estimator achieves superior numerical performance in practice. We show that our estimator enjoys the Oracle property; it identifies the null region with probability tending to 1, and it achieves the same asymptotic normality for the estimated coefficient function on the non-null region as the functional linear model estimator when the non-null region is known. Numerically, our refined estimator overcomes the shortcomings of the initial Dantzig estimator which tends to under-estimate the absolute scale of non-zero coefficients. The performance of the proposed method is illustrated in simulation studies. We apply the method in an analysis of data collected by the Johns Hopkins Precursors Study, where the primary interests are in estimating the strength of association between body mass index in midlife and the quality of life in physical functioning at old age, and in identifying the effective age ranges where such associations exist.
Department of Statistics, University of Virginia, Charlottesville, VA 22904, U.S.A.Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, U.S.A.Department of Statistics, University of Michigan, Ann Arbor, MI 48109, U.S.A.
Jianhui
J
Zhou
Department of Statistics, University of Virginia, Charlottesville, VA 22904, U.S.A.Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, U.S.A.Department of Statistics, University of Michigan, Ann Arbor, MI 48109, U.S.A.
Nae-Yuh
NY
Wang
Department of Statistics, University of Virginia, Charlottesville, VA 22904, U.S.A.Department of Medicine, Johns Hopkins University, Baltimore, MD 21287, U.S.A.Department of Statistics, University of Michigan, Ann Arbor, MI 48109, U.S.A.
Naisyin
N
Wang
B-spline basis function; functional linear regression; group smoothly clipped absolute deviation approach; null region