Resampling time series using missing values techniques

Annals of the Institute of Statistical Mathematics

Volumn 55, Issue 4, 2003, Pages 765-796

  Alonso, Andrés M ^a,b   Peña, Daniel ^b   Romo, Juan ^b  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

^b UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

Bootstrap; Jackknife; Missing values; Time series

Indexed keywords

COMPUTER SIMULATION; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; MONTE CARLO METHODS; PARAMETER ESTIMATION; ROBUSTNESS (CONTROL SYSTEMS);

BOOTSTRAP; JACKKNIFE; MISSING VALUES;

TIME SERIES ANALYSIS;

EID: 11144355295     PISSN: 00203157     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02523392     Document Type: Article

References (35)

1. Abraham, B. and Thavaneswaran, A. (1991). A nonlinear time series model and estimation of missing observations, Ann. Inst. Statist. Math., 43, 493-504.
2. Berk, K. N. (1974). Consistent autoregressive spectral estimates, Ann. Statist., 2, 489-502.
3. Beveridge, S. (1992). Least squares estimation of missing values in time series, Comm. Statist. Theory Methods, 21, 3479-3496.
4. Bose, A. (1990). Bootstrap in moving average models, Ann. Inst. Statist. Math., 42, 753-768.
5. Bosq, D. (1996). Nonparametric Statistics for Stochastic Processes: Estimation and Prediction, Springer, New York.
6. Bühlmann, P. (1995). Moving-average representation of autoregressive approximations, Stochastic Process. Appl., 60, 331-342.
7. Bühlmann, P. (1996). Locally adaptive lag-window spectral estimation, J. Time Ser. Anal., 17, 247-270.
8. Bühlmann, P. (1997). Sieve bootstrap for time series, Bernoulli, 8, 123-148.
9. Bühlmann, P. and Künsch, H. R. (1994). Block length selection in the bootstrap for time series, R.R. No. 72, Eidgenössische Technische Hochschule.
10. Carlstein, E. (1986). The use of subseries values for estimating the variance of a general statistics from a stationary sequence, Ann. Statist., 14, 1171-1194.
11. Carlstein, E., Do, K., Hall, P., Hesterberg, T. and Künsch, H. R. (1998). Matched-block bootstrap for dependent data, Bernoulli, 4, 305-328.
12. Efron, B. (1979). Bootstrap methods: Another look at the jackknife, Ann. Statist., 7, 1-26.
13. Efron, B. and Tibshirani, R. J. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy, Statist. Sci., 1, 54-77.
14. Freedman, D. A. (1984). On bootstrapping two-stage least-square estimates in stationary linear models, Ann. Statist., 12, 827-842.
15. Galbraith, R. F. and Galbraith, J. I. (1974). On the inverse of some patterned matrices arising in the theory of stationary time series, J. Appl. Probab., 11, 63-71.
16. Hannan, E. J. and Kavalieris, L. (1986). Regression, autoregression models, J. Time Ser. Anal., 7, 27-49.
17. Harvey, A. C. and Pierse, R. G. (1984). Estimating missing observations in economic time series, J. Amer. Statist. Assoc., 79, 125-131.
18. Horn, R. A. and Johnson, C. R. (1990). Matrix Analysis, Cambridge University Press, Cambridge.
19. Kreiss, J. P. and Pranke, J. (1992). Bootstrapping stationary autoregressive moving average models, J. Time Ser. Anal., 13, 297-317.
20. Künsch, H. R. (1989). The jackknife and the bootstrap for general stationary observations, Ann. Statist., 17, 1217-1241.
21. Lahiri, S. N. (2002). On the jackknife-after-bootstrap method for dependent data and its consistency properties, Econometric Theory, 18, 79-98.
22. Liu, R. Y. and Singh, K. (1992). Moving blocks jackknife and bootstrap capture weak dependence, Exploring the Limits of Bootstrap (eds. R. Lepage and L. Billard), 225-248, Wiley, New York.
23. Peña, D. (1990). Influential observations in time series, J. Bus. Econom. Statist., 8, 235-241.
24. Peña, D. and Maravall, A. (1991). Interpolation, outliers, and inverse autocorrelations, Comm. Statist. Theory Methods, 20, 3175-3186.
25. Politis, D. N. and Romano, J. F. (1992). A circular block-resampling procedure for stationary data, Exploring the Limits of Bootstrap (eds. R. Lepage and L. Billard), 263-270, Wiley, New York.
26. Politis, D. N. and Romano, J. F. (1994a). Large sample confidence regions based on subsamples under minimal assumptions, Ann. Statist., 22, 2031-2050.
27. Politis, D. N. and Romano, J. F. (1994b). The stationary bootstrap, J. Amer. Statist. Assoc., 89, 1303-1313.
28. Politis, D. N. and Romano, J. F. (1995). Bias-corrected nonparametric spectral estimation, J. Time Ser. Anal., 16, 67-103.
29. Quenouille, M. (1949). Approximation test of correlation in time series, J. Roy. Statist. Soc. Ser. B, 11, 18-84.
30. Schwarz, G. (1978). Estimating the dimension of a model, Ann. Statist., 6, 461-464.
31. Shao, Q.-M. and Yu, H. (1993). Bootstrapping the sample means for stationary mixing sequences, Stochastic Process. Appl., 48, 175-190.
32. Sherman, M. (1998). Efficiency and robustness in subsampling for dependent data, J. Statist. Plann. Inference, 76, 133-146.
33. Tukey, J. (1958). Bias and confidence in not quite large samples, Ann. Math. Statist., 29, p. 614.
34. White, H. (1984). Asymptotic Theory for Econometricians, Academic Press, New York.
35. Wu, C. F. J. (1990). On the asymptotic properties of the jackknife histogram, Ann. Statist., 18, 1438-1452.