A sieve bootstrap two-sample t-Test under serial correlation

Journal of Biopharmaceutical Statistics

Volumn 21, Issue 6, 2011, Pages 1100-1112

  Chen, Bei ^a   Gel, Yulia R ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Dependence effect; Hypothesis testing; Resampling; Serial correlation; Sieve bootstrap; Two sample t test.

Indexed keywords

ARTICLE; BODY WEIGHT; BOOTSTRAPPING; CORRELATION ANALYSIS; ELECTROENCEPHALOGRAM; EMPIRICISM; FUNCTIONAL MAGNETIC RESONANCE IMAGING; LONGITUDINAL STUDY; NONHUMAN; PRIORITY JOURNAL; RAT; SIEVE BOOTSTRAP TWO SAMPLE T TEST; STATISTICAL DISTRIBUTION; STUDENT T TEST;

ANIMALS; BRAIN MAPPING; COMPUTER SIMULATION; HUMANS; MODELS, STATISTICAL; OBSERVER VARIATION; RATS; SAMPLE SIZE; STATISTICS AS TOPIC;

EID: 84862908793     PISSN: 10543406     EISSN: 15205711     Source Type: Journal    
DOI: 10.1080/10543406.2011.610027     Document Type: Article

References (36)

1. Afifi, A. A., Kim, P. J. (1972). Comparison of some two-sample tests for non-normal alternatives. Journal of the Royal Statistical Society, Series B 34(2):448-452.
2. Albers, W. (1978). One-sample rank tests under autoregressive dependence. Annals of Statistics 6:836-845.
3. Barndorff-Nielsen, O. (1997). Processes of normal inverse Gaussian type. Finance and Stochastics 2:41-68.
4. Beran, J., Ghosh, S. (1991). Slowly decaying correlations, testing normality, nuisance parameters. Journal of the American Statistical Association 86:785-791.
5. Brockwell, P. J., Davis, R. A. (2009). Time Series: Theory and Methods. 2nd ed. New York: Springer-Verlag.
6. Brunner, E., Domhof, S., Langer, R. A. (2001). Nonparametric Analysis of Longitudinal Data in Factorial Experiments. New York: Wiley.
7. Bühlman, P. (1997). Sieve bootstrap for time series. Bernoulli 3:123-148.
8. Bühlman, P. (2002). Bootstrap for time series. Statistical Science 17:52-72.
9. Bullmore, E., Long, C., Suckling, J., Fadili, J., Calvert, G., Zelaya, F., Carpenter, T. A., Brammer, M. (2001). Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains. Human Brain Mapping 12:61-78. (Pubitemid 32120705)
10. El-Shaarawi, A. H., Damsleth, E. (1988). Parametric and nonparametric tests for dependent data. Journal of the American Water Resources Association 24:513-519. (Pubitemid 18650481)
11. Chanda, K. C. (1999). Chi-squared tests of goodness-of-fit for dependent observations. Asymptotics, non-parametrics and time series. Statistics: Textbooks and Monographs 158:743-756.
12. Gasser, T. (1975). Goodness-of-fit tests for correlated data. Biometrika 62:563-570.
13. Gastwirth, J. L., Rubin, H. (1971). The behavior of the level of rank tests on dependent data. Journal of the American Statistical Association 66:816-820.
14. Gel, Y. R., Miao, W., Gastwirth, J. L. (2009). The effect of dependence between observations on the proper interpretation of statistical evidence. Law, Probability and Risk 8:25-38.
15. Gerrard, P. D., Schucany, W. R. (2007). An enhanced sign test for dependent binary data with small numbers of clusters. Computational Statistics and Data Analysis 51(9):4622-4632. (Pubitemid 46560916)
16. Gleason, J. R. (1993). The scale contaminated normal family. Journal of American Statistical Association 88(421):327-337.
17. Gleser, L. J., Moore, D. S. (1983). The effect of dependence on chi-squared and empiric distribution tests of fit. Annals of Statistics 11:1100-1108.
18. Good, P. (2010). Permutation, Parametric and Bootstrap Tests of Hypothesis. 3rd ed. New York: Springer.
19. Ignaccolo, R. (2004). Goodness-of-fit tests for dependent data. Journal of Nonparametric Statistics 16:19-38. (Pubitemid 38178255)
20. Jones, R. H. (1990). Longitudinal Data With Serial Correlation: A State-Space Approach. Boca Raton, FL: Chapman & Hall/CRC.
21. Miao, W., Gastwirth, J. L. (2004). The effect of dependence on confidence intervals for a population proportion. American Statistician 58:124-130. (Pubitemid 38614304)
22. Moore, D. S. (1982). The effect of dependence on chi-squared tests of fit. Annals of Statistics 10:1163-1171.
23. Monti, M. M. (2011). Statistical analysis of fMRI time-series: A critical review of the GLM approach. Frontiers of Human Neuroscience 5:28. doi: 10.3389/fnhum.2011.00028.
24. Munk, A., Stockis, J.-P., Valeinis, J., Giese, G. (2011). Neyman smooth goodness-of-fit tests for the marginal distribution of dependent data. Annals of the Institute of Statistical Mathematics 63(5):939-959.
25. Noguchi, K., Gel, Y. R., Duguay, C. R. (2011). Bootstrap-based tests for trends in hydrological time series, with application to ice phenology data. Journal of Hydrology, DOI: 10.1016/j.jhydrol.2011.09.008.
26. Pesaran, M. H., Timmermann, A. (2009). Testing dependence among serially correlated multi-category variables. Journal of the American Statistical Association 104(485): 325-337.
27. Politis, D. N. (2003). The impact of bootstrap methods on time series analysis. Statistical Science 18:219-230. (Pubitemid 37545095)
28. Psaradakis, Z. (2008). Assessing time-reversibility under minimal assumptions. Journal of Time Series Analysis 29:881-905.
29. Psaradakis, Z. (2010). On inference based on the one-sample sign statistic for long-range dependent data. Computational Statistics 25:329-340.
30. Sen, P. K. (1965). Some non-parametric tests for m-dependent time series. Journal of the American Statistical Association 60:134-147.
31. Serfling, R. J. (1968). The Wilcoxon two-sample statistic on strongly mixing processes. Annals of Mathematical Statistics 39(4):1202-1209.
32. Sun, H., Pantula, S. G. (1999). Testing for trends in correlated data. Statistics and Probability Letters 41:87-95.
33. Tukey, J. W. (1960). A survey of sampling from contaminated distribution. In: Olkin, I., et al., eds. Contributions to Probability and Statistics. Stanford, CA: Stanford University Press, pp. 448-485.
34. Verbeke, G., Molenberghs, G. (2009). Linear Mixed Models for Longitudinal Data. 2nd ed. New York: Springer-Verlag.
35. Von Storch, V. H. (1995). Misuses of statistical analysis in climate research. In: von Storch, V. H., Navarra, A., eds. Analysis of Climate Variability Applications of Statistical Techniques. Berlin: Springer-Verlag, pp. 1-26.
36. Wolff, S. S., Gastwirth, J. L., Rubin, H. (1967). The sign test on autoregressive data. IEEE Transaction on Information Theory IT-13:311-313.