next up previous contents index
Next: 3. Design and Analysis Up: csahtml Previous: 2.4 Bootstrap for Dependent

References

1
Beran, R. (1986). Discussion of Wu, C.F.J.: Jackknife, bootstrap, and other resampling methods in regression analysis (with discussion). Ann. Statist., 14:1295-1298.

2
Beran, R. (1988). Prepivoting test statistics: a bootstrap view of asymptotic refinements. J. Amer. Statist. Assoc., 83:687-697.

3
Beran, R. (2003). The Impact of the Bootstrap on Statistical Algorithms and Theory. Statist. Science, 18:175-184.

4
Beran, R. and Ducharme, G. (1991). Asymptotic theory for bootstrap methods in statistics, Les Publications CRM, Univ. Montreal.

5
Bickel, P.J. and Bühlmann, P. (1999). A new Mixing Notion and Functional Central Limit Theorems for a Sieve Bootstrap in Time Series. Bernoulli, 5:413-446.

6
Bickel, P. and Freedman, D. (1981). Some asymptotic theory for the bootstrap, Ann. Statist., 9:1196-1217.

7
Bickel, P. and Freedman, D. (1983). Bootstrapping regression models with many parameters. In Bickel, P.J., Doksum, K.A. and Hodges, J.C. (eds), A Festschrift for Erich L. Lehmann, pp. 28-48, Wadsworth, Belmont.

8
Boos, D.D. (2003). Introduction to the bootstrap world. Statist. Science, 18:168-174.

9
Bose, A. and Chatterjee, S. (2002). Dimension asymptotics for generalized bootstrap in linear regression. Ann. Inst. Statist. Math., 54:367-381.

10
Bühlmann, P. (1997). Sieve Bootstrap for Time Series. Bernoulli, 3:123-148.

11
Bühlmann, P. (1998). Sieve bootstrap for smoothing in nonstationary time series. Ann. Statist., 26:48-83.

12
Bühlmann, P. (2002). Bootstraps for time series. Statist. Science, 17:52-72.

13
Carlstein, E. (1986). The use of subseries methods for estimating the variance of a general statistic from a stationary time series. Ann. Statist., 14:1171-1179.

14
Chan, K.S. (1997). On the validity of the method of surrogate data. Fields Institute Communications, 11:77-97.

15
Choi, E. and Hall, P. (2000). Bootstrap confidence regions computed from auto-regressions of arbitrary order. J. Royal Statist. Soc., Series B, 62:461-477.

16
Dahlhaus, R. and Janas, D. (1996). A frequency domain bootstrap for ratio statistics in time series. Ann. Statist., 24:1934-1963.

17
Davison, A.C. and Hinkley, D.V. (1997). Bootstrap Methods and their Applications, Cambridge University Press, Cambridge.

18
Davison, A.C., Hinkley, D.V. and Young, G.V. (2003). Recent Developments in Bootstrap Methodology. Statist. Science, 18:141-157.

19
Efron, B. (1979). Bootstrap methods: Another look at jackknife. Ann. Statist., 7:1-26.

20
Efron, B. (1982). The Jackknife, the bootstrap, and other Resampling Plans. SIAM, Philadelphia.

21
Efron, B. (2003). Second Thoughts on the Bootstrap, Statist. Science, 18:135-140.

22
Efron, B. and Tibshirani, R.J. (1993) An Introduction to the Bootstrap, Chapman and Hall, London.

23
Franke, J. and Härdle, W. (1992). On bootstrapping kernel spectral estimates. Ann. Statist., 20:121-145.

24
Franke, J. and Kreiss, J.-P. (1992). Bootstrapping ARMA-models. J. Time Series Analysis, 13:297-317.

25
Franke, J., Kreiss, J.-P, and Mammen E. (2002a). Bootstrap of kernel smoothing in nonlinear time series, Bernoulli, 8:1-37.

26
Franke, J., Kreiss, J.-P., Mammen, E. and Neumann, M.H. (2002b). Properties of the Nonparametric Autoregressive Bootstrap. J. Time Series Analysis, 23:555-585.

27
Gine, E. (1997). Lectures on some aspects of the bootstrap. In Bernard, P. (ed), Lectures on Probability Theory and Statistics., Berlin: Springer Lecture Notes Math. 1665, pp. 37-151.

28
Härdle, W. and Mammen, E. (1991). Bootstrap methods for nonparametric regression. In Roussas, G. (ed), Nonparametric Functional estimation and Related Topics, pp. 111-124, Kluwer, Dordrecht.

29
Härdle, W. and Mammen, E. (1993). Testing parametric versus nonparametric regression. Ann. Statist., 21:1926-1947.

30
Härdle, W., Horowitz, J.L. and Kreiss, J.-P. (2003). Bootstrap methods for time series. International Statist. Review, 71:435-459.

31
Hall, P. (1985). Resampling a coverage process. Stoch. Proc. Appl., 19:259-269.

32
Hall, P. (1992). The Bootstrap and Edgeworth Expansions, Springer, New York.

33
Hall, P. (2003). A Short Prehistory of the Bootstrap. Statist. Science, 18:158-167.

34
Hall, P., Horowitz, J.L. and Jing, B.-Y. (1995). On blocking rules for the bootstrap with dependent data. Biometrika, 82:561-574.

35
Hall, P. and Jing, B.-Y. (1996). On sample reuse methods for dependent data. J. Royal Statist. Society, Series B, 58:727-737.

36
Horowitz, J.L. (1997). Bootstrap Methods in Econometrics: Theory and Numerical Performance. In Kreps, D.M. and Wallis, K.F. (eds), Advances in Economics and Econometrics: Theory and Applications, pp. 188-222, Cambridge University Press.

37
Horowitz, J.L. (2001). The Bootstrap. In Heckman, J.J. and Leamer, E.E. (eds), Handbook of Econometrics, vol. 5, Chap. 52, 3159-3228, Elsevier Science B.V.

38
Horowitz, J.L. (2003a). The bootstrap in econometrics. Statist. Science, 18:211-218.

39
Horowitz, J.L. (2003b). Bootstrap Methods for Markov Processes. Econometrica, 71:1049-1082.

40
Inoue, A. and Kilian, L. (2003). The continuity of the limit distribution in the parameter of interest is not essential for the validity of the bootstrap. Econometric Theory, 6:944-961.

41
Janssen, A. and Pauls, T. (2003). How do bootstrap and permutation tests work? Annals of Statistics, 31:768-806.

42
Kreiss, J.-P. (1988). Asymptotic Inference for a Class of Stochastic Processes, Habilitationsschrift. Faculty of Mathematics, University of Hamburg, Germany.

43
Kreiss, J.-P. (1992). Bootstrap procedures for AR($ \infty $) processes. In Jöckel, K.-H., Rothe, G. and Sendler, W. (eds), Bootstrapping and Related Techniques, Lecture Notes in Economics and Mathematical Systems 376, pp. 107-113, Springer, Berlin-Heidelberg-New York.

44
Kreiss, J.-P. and Paparoditis, E. (2003). Autoregressive aided periodogram bootstrap for time series. Ann. Statist., 31:1923-1955.

45
Künsch, H.R. (1989). The jackknife and the bootstrap for general stationary observations. Annals of Statistics, 17:1217-1241.

46
Lahiri, S.N. (1999a). Second order optimality of stationary bootstrap. Statist. Probab. Letters, 11:335-341.

47
Lahiri, S.N. (1999b). Theoretical comparison of block bootstrap methods. Ann. Statist., 27:386-404.

48
Lahiri, P. (2003a). On the Impact of Bootstrap in Survey Sampling and Small-Area Estimation. Statist. Science, 18:199-210.

49
Lahiri, S.N. (2003b). Resampling Methods for Dependent data, Springer, New York.

50
Lehmann, E.L. (1986). Testing Statistical Hypotheses, Springer, New York.

51
Lele, S.R. (2003). Impact of Bootstrap on the Estimating Functions. Statist. Science, 18:185-190.

52
Liu, R.Y. (1988). Bootstrap procedures under some non i.i.d. models. Ann. Statist., 16:1696-1708.

53
Liu, R.Y. and Singh, K. (1992a). Efficiency and robustness in resampling. Ann. Statist., 20:370-384.

54
Liu, R.Y. and Singh, K. (1992b). Moving blocks jackknife and bootstrap capture weak dependence. In Lepage, R. and Billard, L. (eds), Exploring the Limits of the Bootstrap, pp. 225-248, Wiley, New York.

55
Mammen, E. (1989). Asymptotics with increasing dimension for robust regression with applications to the bootstrap. Ann. Statist., 17:382-400.

56
Mammen, E. (1992a). Bootstrap, wild bootstrap, and asymptotic normality. Probab. Theory Related Fields, 93:439-455.

57
Mammen, E. (1992b). When does bootstrap work? Asymptotic results and simulations, Springer Lecture Notes in Statistics 77, Springer, Heidelberg, Berlin.

58
Mammen, E. (1993). Bootstrap and wild bootstrap for high-dimensional linear models. Ann. Statist., 21:2555-285.

59
Mammen, E. (2000). Resampling methods for nonparametric regression. In Schimek, M.G. (ed), Smoothing and Regression: Approaches, Computation and Application, Wiley, New York.

60
Mammen, E. and Nandi, S. (2004). Change of the nature of a test when surrogate data are applied. (To appear in Physical Review E)

61
Mikosch, T. and Starica, C. (2002). Nonstationarities in financial time series, the long range dependence and the IGARCH effects. (Preprint)

62
Neumann, M. and Kreiss, J.-P. (1988). Regression-type inference in nonparametric autoregression. Ann. Statist., 26:1570-1613.

63
Paparoditis, E. (1996). Bootstrapping autoregressive and moving average parameter estimates of infinite order vector autoregressive processes. J. Multivariate Anal., 57:277-296.

64
Paparodtis, E. and Politis, D.N. (2000). The local bootstrap for kernel estimators under general dependence conditions. Ann. Inst. Statist. Math., 52:139-159.

65
Paparoditis, E. and Politis, D.N. (2001a). Tapered block bootstrap. Biometrika, 88:1105-1119.

66
Paparoditis, E. and Politis, D.N. (2001b). A Markovian local resampling scheme for nonparametric estimators in time series analysis. Econometric Theory, 17:540-566.

67
Paparoditis, E. and Politis D.N. (2002a). The tapered block bootstrap for general statistics from stationary sequences. The Econometrics Journal, 5:131-148.

68
Paparoditis, E. and Politis, D.N. (2002b). The local bootstrap for Markov processes. J. Statist. Planning Inference, 108:301-328.

69
Park, J.Y. (2002). An invariance principle for sieve bootstrap in time series, Econometric Theory, 18:469-490.

70
Politis, D.N. (2003). The Impact of Bootstrap Methods on Time Series Analysis. Statist. Science, 18:219-230.

71
Politis, D.N. and Romano, J.P. (1994). The Stationary Bootstrap. J. Amer. Statist. Assoc., 89:1303-1313.

72
Politis, D.N., Romano, J.P. and Wolf, M. (1999). Subsampling, Springer, New York.

73
Rajarshi, M.B. (1990). Bootstrap in Markovsequences based on estimates of transition density. Ann. Inst. Staitst. Math., 42:253-268.

74
Shao, J. (1996). Resampling methods in sample surveys (with discussions). Statistics, 27:203-254.

75
Shao, J. (2003). Impact of the Bootstrap on Sample Surveys. Statist. Science, 18:191-198.

76
Shao, J. and Tu, T. (1995). The Jackknife and Bootstrap, Springer, New York.

77
Theiler, J., Eubank, S., Longtin, A., Galdrikan, B. and Farmer, J.D. (1992). Testing for nonlinearity in time series: the method of surrogate data. Physica D, 58:77-94.

78
Wu, C.F.J. (1986). Jackknife, bootstrap, and other resampling methods in regression analysis (with discussion). Ann. Statist., 14:1261-1295.



Subsections