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Diagnosis of Observations after Fit of Multivariate Skew t-Distribution: Identification of Outliers and Edge Observations from Asymmetric Data
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 Title & Authors
Diagnosis of Observations after Fit of Multivariate Skew t-Distribution: Identification of Outliers and Edge Observations from Asymmetric Data
Kim, Seung-Gu;
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 Abstract
This paper presents a method for the identification of "edge observations" located on a boundary area constructed by a truncation variable as well as for the identification of outliers and the after fit of multivariate skew -distribution(MST) to asymmetric data. The detection of edge observation is important in data analysis because it provides information on a certain critical area in observation space. The proposed method is applied to an Australian Institute of Sport(AIS) dataset that is well known for asymmetry in data space.
 Keywords
Multivariate skew t-distribution;edge observation;outlier;ECM algorithm;
 Language
Korean
 Cited by
1.
치우친 다변량 t-분포 혼합모형에 대한 최우추정,김승구;

응용통계연구, 2014. vol.27. 5, pp.819-831 crossref(new window)
1.
An Alternating Approach of Maximum Likelihood Estimation for Mixture of Multivariate Skew t-Distribution, Korean Journal of Applied Statistics, 2014, 27, 5, 819  crossref(new windwow)
 References
1.
Azzalini, A. (1985). A class of distribution which includes the normal ones, Scandinavian Journal of Statistics, 33, 561-574.

2.
Azzalini, A. and Dalla-Valle, A. (1996). The multivariate skew normal distribution, Biometrika, 83, 715-726. crossref(new window)

3.
Bickel, P. J. and Doksum, K. A. (1981). An analysis of transformations revisited, Journal of American Statistical Association, 76(374), 296-311. crossref(new window)

4.
Cabral, C. S., Lachos, V. H. and Prates, M. O. (2012). Multivariate mixture modeling using skew-normal independent distribution, Computational Statistics and Data Analysis, 56, 126-142. crossref(new window)

5.
Cook, R. D. and Weisberg, S. (1994). An Introduction to Regression Graphics, 56, Wiley, New York.

6.
Ho, H. J., Lin, T. I., Chen, H.-Y. and Wang, W.-L. (2012). Some results on the truncated multivariate t distribution, Journal of Statistical Planning & Inference, 142, 25-40. crossref(new window)

7.
Kim, H. J. (2008). Moments of truncated Student-t distribution, Journal of Korean Statistical Society, 37, 81-87. crossref(new window)

8.
Kim, S.-G. (2012). ECM Algorithm for fitting of mixtures of multivariate Skew t-Distribution, Communications of the Korean Statistical Society, 19, 673-684. crossref(new window)

9.
Lachos, V. H., Ghosh, P. and Arellano-Valle, R. B. (2010). Likelihood based inference for skew-normal independent linear mixed model, Statistica Sinica, 20, 303-322.

10.
Lin, T.-I. (2010). Robust mixture modeling using multivariate skew t distributions, Statistics and Computing, 20, 343-356. crossref(new window)

11.
Lo, K., Brinkman, R. R. and Gottardo, R. (2008). Automated gating of ow cytometry data via robust model-based clustering. Cytometry Part A, 73, 321-332.

12.
Lo, K. and Gottardo, R. (2012). Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: An alternative to the skew-t distribution, Statistics and Computing, 22, 33-52. crossref(new window)

13.
McLachlan, G. J. and Peel, D. (2000). Finite Mixture Models, Wiley, New York.

14.
Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T. I., Maier, L., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Ha er, D. A., De Jager, P. L. and Mesirov, J. P. (2009). Automated high-dimensional ow cytometric data analysis, Proceedings of the National Academy of Sciences of the United States of America, 106, 8519-8524. crossref(new window)

15.
Sahu, S. K., Dey, D. K. and Branco, M. D. (2003). A new class of multivariate skew distribution with application to Bayesian regression model, The Canadian Journal of Statistics, 31, 129-150. crossref(new window)