an:06446515
Zbl 1388.62121
Zhu, Li-Ping; Zhu, Li-Xing; Feng, Zheng-Hui
Dimension reduction in regressions through cumulative slicing estimation
EN
J. Am. Stat. Assoc. 105, No. 492, 1455-1466 (2010).
00274349
2010
j
62G08 62H12 62H25 62F12
inverse regression; ultrahigh dimensionality; semiparametric modeling
Summary: In this paper, we offer a complete methodology of cumulative slicing estimation to sufficient dimension reduction. In parallel to the classical slicing estimation, we develop three methods that are termed, respectively, as cumulative mean estimation, cumulative variance estimation, and cumulative directional regression. The strong consistency for \(p=O(n^{1/2}/\log n)\) and the asymptotic normality for \(p=o(n^{1/2})\) are established, where \(p\) is the dimension of the predictors and \(n\) is sample size. Such asymptotic results improve the rate \(p=o(n^{1/3})\) in many existing contexts of semiparametric modeling. In addition, we propose a modified BIC-type criterion to estimate the structural dimension of the central subspace. Its consistency is established when \(p=o(n^{1/2})\). Extensive simulations are carried out for comparison with existing methods and a real data example is presented for illustration.