Outlier Detection from a Mixture Distribution When Training Data Are Unlabeled

Bulletin of the Seismological Society of America

Volumn 89, Issue 1, 1999, Pages 294-304

  Sain, Stephan R ^a   Gray, H L ^a   Woodward, Wayne A ^a   Fisk, Mark D ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0033437383     PISSN: 00371106     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (31)

1. Akaike, H. (1974). A new look at the statistical model identification, IEEE Trans. Automatic Control 19, 716-723.
2. Akaike, H. (1977). On entropy maximization principle, Applications of Statistics, P. R. Krishnaiah (Editor), North-Holland, Amsterdam, 27-41.
3. Baumgardt, D. R. J. Carney, M. Maxson, and S. Cartor (1992). Evaluation of regional discriminants using the intelligent seismic event identification system, semi-annual technical report SAS-TR-93-38, ENSCO, Inc., Springfield, Virginia.
4. Bozdogan, H. and S. L. Sclove (1984). Multi-sample cluster analysis using Akaike's information criterion, Ann. Inst. Stat. Math. 36, 163-180.
5. Burlacu, R. and E. Herrin (1996). Discrimination between earthquakes and ripple-fired explosions based on a parametric spectral approach, unpublished manuscript.
6. Dargahi-Noubary, G. R. (1995). Stochastic modeling and identification of seismic records based on established deterministic formulations, J. Time Series Anal. 16, 201-219.
7. Dempster, A. P., N. M. Laird, and D. B. Rubin (1977). Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion), J. R. Stat. Soc. B39, 1-38.
8. Efron, B. and R. J. Tibshirani (1993). An Introduction to the Bootstrap, Chapman and Hall, New York.
9. Fisk, M. D., H. L. Gray, and G. D. McCartor (1994). Preliminary assessment of seismic CTBT/NPT monitoring capability, PL-TR-94-2300, Phillips Laboratory, Hanscom Air Force Base, Massachusetts.
10. Fisk, M. D., H. L. Gray, and G. McCartor (1996). Regional event discrimination without transporting thresholds, Bull. Seism. Soc. Am. 86, 1545-1558.
11. Fisk, M. D., S. Bottone, R. J. Carlson, H. L. Gray, and G. D. McCartor (1998). Event characterization development and analysis at the prototype IDC, MRC-R-1548, Mission Research Corp., Santa Barbara, California (to appear as a Phillips Laboratory Technical Report).
12. Forgy, E. W. (1965). Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21, 768-769.
13. Grant. L., J. Coyne, and F. Ryall (1993). CSS ground truth database: version 1 handbook, Scientific Report, C93-05, Center for Seismic Studies.
14. Gray, H. L., W. A. Woodward, and G. McCartor (1989). Testing for the maximum mean in a mixture of normals, Commun. Stat. A18, 4011-4028.
15. Hannan, E. J. (1980). The estimation of the order of an ARMA process, Ann. Stat. 8, 1071-1081.
16. Hartigan, J. A. (1975). Clustering Algorithms, Wiley, New York.
17. Hartse, H. E., S. R. Taylor, W. S. Phillips, and G. E. Randall (1997). A preliminary study of regional seismic discrimination in central Asia with emphasis on western China, Bull. Seism. Soc. Am. 87, 551-568.
18. Jain, A. K. and R. C. Dubes (1988). Algorithms for Clustering Data, Prentice Hall, Englewood Cliffs, New Jersey.
19. McLachlan, G. J. (1982). The classification and mixture maximum likelihood approaches to clustering, in Handbook of Statistics, Vol. 2, P. R. Krishnaiah and L. Kanal (Editors), North-Holland, Amsterdam, 199-208.
20. McLachlan, G. J. (1987). On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture, Appl. Stat. 36, 318-324.
21. McLachlan, G. J. and T. Krishnan (1997). The EM Algorithm and Extensions, Wiley, New York.
22. Redner, R. A. and H. F. Walker (1984). Mixture densities, maximum likelihood and the EM algorithm, SIAM Rev. 26, 195-239.
23. Redner, R. A., G. Kitagawa, and W. A. Coberly (1981). The Akaike information criterion and its application to mixture proportion estimation, NASA Technical Report No. SR-TI-04207.
24. Sain, S. R., H. L. Gray, W. A. Woodward, and M. D. Fisk (1996). Outlier detection without ground truth, poster presentation at the 18th Annual Seismic Research Symposium on Monitoring a Comprehensive Test Ban Treaty, 4-6 September 1996.
25. Sclove, S. L. (1993) Application of the conditional population-mixture model to image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-5, 428-433.
26. Seber, G. A. F. (1984). Multivariate Observations, Wiley. New York.
27. Taylor, S. R. and H. E. Hartse (1997). An evaluation of generalized likelihood ratio outlier detection to identification of seismic events in western China, Bull. Seism. Soc. Am. 87, 824-831.
28. Tjøstheim, D. (1975). Some autoregressive models for short-period seismic noise, Bull. Seism. Soc. Am. 65, 677-691.
29. Walter, W. R., K. M. Mayeda, H. J. Patton (1995). Phase and spectral ratio discrimination between NTS earthquakes and explosions. Part I: empirical observations, Bull. Seism. Soc. Am. 85, 1050-1067.
30. Wang, S., W. A. Woodward, H. L. Gray, S. Wiechecki, and S. R. Sain (1997). A new test for outlier detection from a multivariate mixture distribution, J. Computation. Graph. Stat. 6, 285-299.
31. Wüster, J. (1993). Discrimination of chemical explosions and earthquakes in central Europe - a case study, Bull. Seism. Soc. Am. 83, 1184-1212.