A smoothed bootstrap test for independence based on mutual information

Computational Statistics and Data Analysis

Volumn 53, Issue 7, 2009, Pages 2524-2536

  Wu, Edmond H C ^a   Yu, Philip L H ^a   Li, W K ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; INDEPENDENT COMPONENT ANALYSIS; TIME SERIES;

HIGH POWER; HIGHER DIMENSIONS; INDEPENDENT COMPONENTS; MULTIVARIATE TIME SERIES; MUTUAL INFORMATIONS; NULL DISTRIBUTIONS; SMOOTHED BOOTSTRAPS; STATIONARY TIME SERIES;

TESTING;

EID: 61849125855     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2008.11.032     Document Type: Article

References (32)

1. Ahmad I.A., and Li Q. Testing independence by nonparametric kernel method. Statist. Probab. Lett. 34 (1997) 201-210
2. Bell A., and Sejnowski T. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7 6 (1995) 1004-1034
3. Blum J.R., Kiefer J., and Rosenblatt M. Distribution free tests of independence based on the sample distribution function. Ann. Math. Statist. 32 (1961) 485-498
4. Bühlmann P. Sieve bootstrap for time series. Bernoulli 3 (1997) 123-148
5. Bühlmann P., and Künsch H.R. Block length selection in the bootstrap for time series. Comput. Statist. Data Anal. 31 3 (1999) 295-310
6. Cardoso J.F., and Souloumiac A. Blind beamforming for non-Gaussian signals. Proc. Inst. Elect. Eng. F. 140 6 (1993) 362-370
7. Comon P. Independent component analysis: A new concept?. Signal Process. 36 (1994) 287-314
8. Duin R.P.W. On the choice of smoothing parameters for Parzen estimators of probability density functions. IEEE Trans. Comput. C-25 (1976) 1175-1179
9. Efron B., and Tibshirani R.J. An Introduction to the Bootstrap (1993), Chapman & Hall, New York
10. Fukunaga K. Introduction to Statistical Pattern Recognition (1972), Academic Press
11. Hall P., Sheather S.J., Jones M.C., and Marron J.S. On optimal data-based bandwidth selection in kernel density estimation. Biometrika 78 (1991) 263-269
12. Hong Y., and White H. Asymptotic distribution theory for nonparametric entropy measures of serial dependence. Econometrika 73 (2005) 837-901
13. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10 3 (1999) 626-634
14. Hyvärinen A., Karhunen J., and Oja E. Independent Component Analysis (2001), J. Wiley, New York
15. Jones M.C., Marron J.S., and Sheather S.J. A brief survey of bandwidth selection for density estimation. J. Amer. Statist. Assoc. 91 433 (1996) 401-407
16. Kankainen A., and Ushakov N.G. A consistent modification of a test for independence based on the empirical characteristic function. J. Math. Sci. 89 (1998) 1582-1589
17. Karvanen J. A resampling test for the total independence of stationary time series: Application to the performance evaluation of ICA algorithms. Neural Process. Lett. 22 3 (2005) 311-324
18. Kojadinovic I. Agglomerative hierarchical clustering of continuous variables based on mutual information. Comput. Statist. Data Anal. 46 (2004) 269-294
19. Ku C.J., and Fine T.L. Testing for stochastic independence: Application to blind source separation. IEEE Trans. Signal Process. 53 5 (2005) 1815-1826
20. La Rocca M., and Perna C. Variable selection in neural network regression models with dependent data: A subsampling approach. Comput. Statist. Data Anal. 48 2 (2005) 415-429
21. Meintanis S., and Illopoulos G. Fourier methods for testing multivariate independence. Comput. Statist. Data Anal. 52 (2008) 1884-1895
22. Moon Y., Rajagopalan B., and Lall U. Estimation of mutual information using kernel density estimators. Phys. Rev. E 52 3 (1995) 2318-2321
23. Robinson P.M. Consistent nonparametric entropy-based testing. Rev. Econom. Studies 58 (1991) 437-453
24. Rosenblatt M., and Wahlen B.E. A nonparametric measure of independence under a hypothesis of independence components. Statist. Probab. Lett. 15 (1992) 245-252
25. Shao J., and Tu D. The Jackknife and Bootstrap (1995), Springer-Verlag
26. Shimizu S., Hyvärinen A., Hoyer P.O., and Kano Y. Finding a causal ordering via independent component analysis. Comput. Statist. Data Anal. 50 (2006) 3278-3293
27. Silverman B.W. Density Estimation for Statistics and Data Analysis (1986), Chapmann & Hall, London
28. Singh K. On the asymptotic accuracy of Efron'S bootstrap. Ann. Statist. 9 (1981) 1187-1195
29. Steuer R., Daub C.O., Selbig J., and Kurths J. Measuring distances between variables by mutual information. In: Baier D., and Wernecke K.D. (Eds). Innovations in Classification, Data Science, and Information Systems (2005), Springer 81-90
30. Taskinen S., Sirkiä S., and Oja H. Independent component analysis based on symmetrised scatter matrices. Comput. Statist. Data Anal. 51 (2007) 5103-5111
31. Terrell G.R. The maximal smoothing principle in density estimation. J. Amer. Statist. Assoc. 85 410 (1990) 470-477
32. Zheng X. A consistent test of conditional parametric distributions. Econom. Theory 16 (2000) 667-691