|
[1] P. Abry, P. Flandrim, M. Taqqu and D. Veitch, Self-similarity and long-range dependence through the wavelet lens, Theory and Applications of Long-Range Dependence, P. Doukhan, G. Oppenheim and M. Taqqu, eds., Birkhäuser, Boston, 2003.
[2] P. Abry, D. Veitch and P. Flandrim, Long-range dependence: revisiting aggregation with wavelets, Journal of Time Series Analysis 19(3) (1998), 256-266.
[3] R. G. Andrzejak, A. Kraskov, H. Stögbauer, F. Mormann and T. Kreuz, Bivariate surrogate techniques: necessity, strengths and caveats, Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics 68(6) (2003), 066202.
[4] C. Angelini, D. Cava, G. Katul and B. Vidakovic, Resampling hierarchical processes in the wavelet domain: A case study using atmospheric turbulence, Physica D: Nonlinear Phenomena 207(1-2) (2005), 24-40.
[5] L. Bao and S. S. Intille, Activity recognition from user-annotated acceleration data, Proceedings of the 2nd International Conference on Pervasive Computing (PERVASIVE’04), Linz/Vienna, Austria, Springer, 2004.
[6] L. Barnard, J. S. Yi, J. A. Jacko and A. Sears, An empirical comparison of use-in-motion evaluation scenarios for mobile computing devices, International Journal of Human-Computer Studies 62(4) (2005), 487-520.
[7] L. Barnard, J. S. Yi, J. A. Jacko and A. Sears, Capturing the effects of context on human performance in mobile computing systems, Personal and Ubiquitous Computing 11(2) (2007), 81-96.
[8] J. Beran, Statistics for Long Memory Processes, Chapman & Hall, 1994.
[9] M. Breakspear, M. Brammer and P. A. Robinson, Construction of multivariate surrogate sets from nonlinear data using the wavelet transform, Physica D: Nonlinear Phenomena 182(1-2) (2003), 1-22.
[10] P. Bühlmann, Bootstraps for time series, Statistical Science 17(1) (2002), 52-72.
[11] E. Bullmore, J. Fadili, M. Breakspear, R. Salvador, J. Suckling and M. Brammer, Wavelets and statistical analysis of functional magnetic resonance images of the human brain, Statistical Methods in Medical Research 12 (2003), 375-399.
[12] E. Bullmore, C. Long, J. Suckling, J. Fadili, G. Calvert, F. Zelaya, T. A. Carpenter and M. Brammer, Colored noise and computational inference in neurophysiological (fmri) time series analysis: Resampling methods in time and wavelet domains, Human Brain Mapping 12(2) (2001), 61-78.
[13] M. M. Clayton and H. C. Schamhardt, Measurement techniques for gait analysis, Equine Locomotion, W. Back and H. M. Clayton, eds., W. B. Saunders Company, 2000.
[14] A. Crossan, R. Murray-Smith, S. Brewster, J. Kelly and B. Musizza, Gait phase effects in mobile interaction, Conference on Human Factors in Computing Systems (CHI’05), Portland, OR, USA, 2005, ACM Press, pp. 1312-1315.
[15] A. C. Davison and D. V. Hinkley, Bootstrap Methods and their Application, Cambridge University Press, 1997.
[16] B. Efron, Bootstrap methods: Another look at the jackknife, The Annals of Statistics 7 (1979), 1-26.
[17] B. Efron and R. Tibshirani, An introduction to the bootstrap, Chapman and Hall, 1993.
[18] P. Fladrin, Wavelet analysis and synthesis of fractional brownian motion, IEEE Transactions on Information Theory 38(2) (1992), 910-917.
[19] M. J. Griffin, Handbook of Human Vibration, Academic Press, 1996.
[20] S. S. Intille, L. Bao, E. M. Tapia and J. Rondoni, Acquiring in situ training data for context-aware ubiquitous computing applications, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’04), Vienna, Austria, 2004, ACM Press, pp. 1-8.
[21] Y. Kim, J. Yi and K. Rhee, Acceleration patterns of the body center in level walking: Normal and hemiplegic patients, World Congress on Medical Physics and Biomedical Engineering, Sydney, Australia, 2003.
[22] H. R. Künsch, The jackknife and the bootstrap for general stationary observations, The Annals of Statistics 17(3) (1989), 1217-1241.
[23] N. Mordant, J. Delour, E. Leveque, O. Michel, A. Arnedo and J. Printon, Lagrangian velocity fluctuations in fully developed turbulence: scaling, intermittency, and dynamics, Journal of Statistical Physics 113(5/6) (2003), 701-717.
[24] D. Percival, S. Sardy and A. Davison, Wavestrapping time series: adaptive wavelet-based bootstrapping, Nonlinear and Nonstationary Signal Processing, W. Fitzgerald, R. Smith, A. Walden and P. Young, eds., Cambridge University Press, 1992.
[25] D. N. Politis and J. P. Romano, The stationary bootstrap, Journal of the American Statistical Association 89(428) (1994), 1303-1313.
[26] J. Theiler, S. Eubank, A. Longtin, B. Galdrikian and J. D. Farmer, Testing for nonlinearity in time series: the method of surrogate data, Physica D 58(1-4) (1992), 77-94.
[27] M. Uiterwaal, E. Glerum, H. Busser and R. van Lummel, Ambulatory monitoring of physical activity in working situations, a validation study, Journal of Medical Engineering & Technology 22(4) (1998), 168-172.
[28] B. Vidakovic, Statistical Modeling by Wavelets, Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., 1999.
[29] B. Vidakovic and C. Lozoya, On time-dependent wavelet denoising, IEEE Transactions on Signal Processing 46(9) (1998), 2549-2554.
[30] J. S. Yi, Y. S. Choi, J. A. Jacko and A. Sears, Context awareness via a single device-attached accelerometer during mobile computing, Proceedings of the 7th International Conference on Human Computer Interaction with Mobile Devices and Services (Mobile HCI’05), Salzburg, Austria, 2005, pp. 303-306. |
Home
