Testing the significance of spatio-temporal teleconnection patterns

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2012, Pages 642-650

  Kawale, Jaya ^a   Chatterjee, Snigdhansu ^a   Ormsby, Dominick ^a   Steinhaeuser, Karsten ^a   Liess, Stefan ^a   Kumar, Vipin ^a  

^a UNIVERSITY OF MINNESOTA   (United States)

Author keywords

significance testing

Indexed keywords

CLIMATE DATA; EL NINO; LONG-DISTANCE CONNECTION; PRESSURE ANOMALY; SEASONALITY; SIGNIFICANCE TESTING; SPATIO-TEMPORAL; SPATIO-TEMPORAL DATA MINING; STATISTICAL SIGNIFICANCE; TELECONNECTION PATTERNS; TELECONNECTIONS; TEMPERATURE ANOMALY; WILD BOOTSTRAP;

AUTOCORRELATION;

DATA MINING;

EID: 84866011194     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2339530.2339634     Document Type: Conference Paper

References (28)

1. J.M. Wallace and D.S. Gutzler. Téléconnexions in the geopotential height field during the northern hemisphere winter. Mon. Wea. Rev, 109:784-812, 1981.
2. H. Von Storch and F.W. Zwiers. Statistical analysis in climate research. Cambridge Univ Pr, 2002.
3. J. Kawale, S. Liess, A. Kumar, M. Steinbach, A. Ganguly, N.F. Samatova, F. Semazzi, P. Snyder, and V. Kumar. Data guided discovery of dynamic climate dipoles. In CIDU, pages 30-44, 2011.
4. J. Kawale, M. Steinbach, and V. Kumar. Discovering dynamic dipoles in climate data. In SIAM Conference on Data Mining, SDM, pages 107-118, 2011.
5. M. Steinbach, P.N. Tan, V. Kumar, S. Klooster, and C. Potter. Discovery of climate indices using clustering. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 446-455. ACM, 2003.
6. E.S. Edgington. Randomization tests, volume 147. CRC Press, 1995.
7. S. Hanhijarvi, G. Garriga, and K. Puolamaki. Randomization techniques for graphs. 2009.
8. A. Gionis, H. Mannila, T. Mielik"ainen, and P. Tsaparas. Assessing data mining results via swap randomization. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(3):14-es, 2007.
9. Nuno Castro and Paulo J. Azevedo. Time series motifs statistical significance. In SDM, pages 687-698, 2011.
10. P. Ferreira, P. Azevedo, C. Silva, and R. Brito. Mining approximate motifs in time series. In Discovery Science, pages 89-101. Springer, 2006.
11. T. Oates. Peruse: An unsupervised algorithm for finding recurrig patterns in time series. 2002.
12. W. Ulrich and N.J. Gotelli. Null model analysis of species nestedness patterns. Ecology, 88(7):1824-1831, 2007.
13. A. Waldron. Null models of geographic range size evolution reaffirm its heritability. American Naturalist, pages 221-231, 2007.
14. J.A. Veech. A null model for detecting nonrandom patterns of species richness along spatial gradients. Ecology, 81(4):1143-1149, 2000.
15. J. Besag and P.J. Diggle. Simple monte carlo tests for spatial pattern. Applied Statistics, pages 327-333, 1977.
16. D.S. Wilks. Statistical methods in the atmospheric sciences, volume 100. Academic press, 2011.
17. R.F. Engle and C.W.J. Granger. Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society, pages 251-276, 1987.
18. C.W.J. Granger. Some properties of time series data and their use in econometric model specification. Journal of econometrics, 16(1):121-130, 1981.
19. E.C. Weatherhead, G.C. Reinsel, G.C. Tiao, X.L. Meng, D. Choi, W.K. Cheang, T. Keller, J. DeLuisi, D.J. Wuebbles, J.B. Kerr, et al. Factors affecting the detection of trends: Statistical considerations and applications to environmental data. Journal of Geophysical Research, 103(D14):17-149, 1998.
20. C.F.J. Wu. Jackknife, bootstrap and other resampling methods in regression analysis. The Annals of Statistics, 14(4):1261-1295, 1986.
21. E. Mammen. Bootstrap and wild bootstrap for high dimensional linear models. The Annals of Statistics, 21(1):255-285, 1993.
22. R.A. Fisher et al. On the" probable error" of a coefficient of correlation deduced from a small sample. Metron, 1:3-32, 1921.
23. W. Hardle and E. Mammen. Comparing nonparametric versus parametric regression fits. The Annals of Statistics, 21(4):1926-1947, 1993.
24. Y. Benjamini and D. Yekutieli. The control of the false discovery rate in multiple testing under dependency. Annals of statistics, pages 1165-1188, 2001.
25. E. Kalnay and et al. The ncep/ncar 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77:437-471, 1996.
26. Climate prediction centre, http://www.cpc.ncep.noaa.gov/.
27. J.E. Janowiak. An investigation of interannual rainfall variability in africa. Journal of Climate, 1:240-255, 1988.
28. K.M. Hines, D.H. Bromwich, and G.J. Marshall. Artificial surface pressure trends in the ncep-ncar reanalysis over the southern ocean and antarctica. J. Climate, 13(22):3940-3952, 2000.