Advanced SearchSearch Tips
Diagnosis of Observations after Fit of Multivariate Skew t-Distribution: Identification of Outliers and Edge Observations from Asymmetric Data
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Diagnosis of Observations after Fit of Multivariate Skew t-Distribution: Identification of Outliers and Edge Observations from Asymmetric Data
Kim, Seung-Gu;
  PDF(new window)
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.
Multivariate skew t-distribution;edge observation;outlier;ECM algorithm;
 Cited by
치우친 다변량 t-분포 혼합모형에 대한 최우추정,김승구;

응용통계연구, 2014. vol.27. 5, pp.819-831 crossref(new window)
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)
Azzalini, A. (1985). A class of distribution which includes the normal ones, Scandinavian Journal of Statistics, 33, 561-574.

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

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)

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)

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

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)

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

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)

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.

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

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.

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)

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

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)

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)