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A Short Note on Empirical Penalty Term Study of BIC in K-means Clustering Inverse Regression
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 Title & Authors
A Short Note on Empirical Penalty Term Study of BIC in K-means Clustering Inverse Regression
Ahn, Ji-Hyun; Yoo, Jae-Keun;
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 Abstract
According to recent studies, Bayesian information criteria(BIC) is proposed to determine the structural dimension of the central subspace through sliced inverse regression(SIR) with high-dimensional predictors. The BIC may be useful in K-means clustering inverse regression(KIR) with high-dimensional predictors. However, the direct application of the BIC to KIR may be problematic, because the slicing scheme in SIR is not the same as that of KIR. In this paper, we present empirical penalty term studies of BIC in KIR to identify the most appropriate one. Numerical studies and real data analysis are presented.
 Keywords
Bayesian information;inverse regression;multivariate regression;K-means clustering;
 Language
English
 Cited by
 References
1.
Bura, E. and Cook, R. D. (2001). Extended sliced inverse regression: The weighted chi-squared test, Journal of the American Statistical Association, 96, 996-1003. crossref(new window)

2.
Cook, R. D. (1998). Regression Graphics, Wiley, New York.

3.
Cook, R. D. and Ni, L. (2005). Sufficient dimension reduction via inverse regression: A minimum discrepancy approach, Journal of the American Statistical Association, 100, 410-428. crossref(new window)

4.
Cook, R. D. and Weisberg, S. (1983). Diagnostics for heteroscedasticity in regression, Biometrika, 70, 1-10. crossref(new window)

5.
Li, K. C. (1991). Sliced inverse regression for dimension reduction, Journal of the American Statisti- cal Association, 86, 316-342. crossref(new window)

6.
Schwarz, G. (1978). Estimating the dimension of a model, The Annals of Mathematical Statististics, 30, 461-464.

7.
Setodji, C. M. and Cook, R. D. (2004). K-means inverse regression, Technometrics, 46, 421-429. crossref(new window)

8.
Zhu, L., Miao, B. and Peng, H. (2006). On sliced inverse regression with high-dimensional covariates, Journal of the American Statistical Association, 101, 630-643. crossref(new window)