TY - JOUR
T1 - On hybrid methods of inverse regression-based algorithms
AU - ZHU, Lixing
AU - Ohtaki, Megu
AU - Li, Yingxing
N1 - Funding Information:
The research described here was supported by a Grant (HKBU7058/05P) from the Research Grants Council of Hong Kong, Hong Kong. The authors thank the Editor, the Associate Editor and the referee for their constructive comments and suggestion which led to an improvement of the presentation of the earlier manuscript.
PY - 2007/2/1
Y1 - 2007/2/1
N2 - This paper is two-fold. First, we present a further investigation for the hybrid methods of inverse regression-based algorithms. This investigation provides the evidence of how the hybrids gain the advantages to become more powerful methods than the existing methods when the central dimension reduction (CDR) space is estimated. Second, a Bayes Information Criterion (BIC)-type algorithm is recommended to estimate the dimension of the CDR space. Differing from the popularly used sequential test methods, the new algorithm does not require the asymptotic normality of the estimator of the inverse regression-based matrix. The BIC-based estimator is proven to be consistent. A set of simulations for several typical models were carried out to guide the selection of coefficient in the hybrids.
AB - This paper is two-fold. First, we present a further investigation for the hybrid methods of inverse regression-based algorithms. This investigation provides the evidence of how the hybrids gain the advantages to become more powerful methods than the existing methods when the central dimension reduction (CDR) space is estimated. Second, a Bayes Information Criterion (BIC)-type algorithm is recommended to estimate the dimension of the CDR space. Differing from the popularly used sequential test methods, the new algorithm does not require the asymptotic normality of the estimator of the inverse regression-based matrix. The BIC-based estimator is proven to be consistent. A set of simulations for several typical models were carried out to guide the selection of coefficient in the hybrids.
KW - Dimension reduction
KW - Hybrid methods
KW - Sliced average variance estimation
KW - Sliced inverse regression
UR - http://www.scopus.com/inward/record.url?scp=33751014301&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2006.01.005
DO - 10.1016/j.csda.2006.01.005
M3 - Journal article
AN - SCOPUS:33751014301
SN - 0167-9473
VL - 51
SP - 2621
EP - 2635
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 5
ER -