The Impact of Bootstrap Methods on Time Series Analysis

Singh, K. (1981). On the asymptotic accuracy of Efron's bootstrap. <i>Ann. Statist.</i> <b>9</b> 1187--1195.

Efron, B. (1979). Bootstrap methods: Another look at the jackknife. <i>Ann. Statist.</i> <b>7</b> 1--26.

Carlstein, E. (1986). The use of subseries values for estimating the variance of a general statistic from a stationary time series. <i>Ann. Statist.</i> <b>14</b> 1171--1179.

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

Künsch, H. R. (1989). The jackknife and the bootstrap for general stationary observations. <i>Ann. Statist.</i> <b>17</b> 1217--1241.

Politis, D. N. and Romano, J. P. (1995). Bias-corrected nonparametric spectral estimation. <i>J. Time Ser. Anal.</i> <b>16</b> 67--103.

Bose, A. (1988). Edgeworth correction by bootstrap in autoregressions. <i>Ann. Statist.</i> <b>16</b> 1709--1722.

Hall, P. (1985). Resampling a coverage pattern. <i>Stochastic Process. Appl.</i> <b>20</b> 231--246.

Lahiri, S. N. (1991). Second order optimality of stationary bootstrap. <i>Statist. Probab. Lett.</i> <b>11</b> 335--341.

Shibata, R. (1976). Selection of the order of an autoregressive model by Akaike's information criterion. <i>Biometrika</i> <b>63</b> 117--126.

Dahlhaus, R. (1997). Fitting time series models to nonstationary processes. <i>Ann. Statist.</i> <b>25</b> 1--37.

Freedman, D. A. (1981). Bootstrapping regression models. <i>Ann. Statist.</i> <b>9</b> 1218--1228.

Politis, D. N., Romano, J. P. and Wolf, M. (1999). <i>Subsampling</i>. Springer, New York.

Hall, P. (1992). <i>The Bootstrap and Edgeworth Expansion</i>. Springer, New York.

Efron, B. and Tibshirani, R. J. (1993). <i>An Introduction to the Bootstrap</i>. Chapman and Hall, New York.

Liu, R. Y. and Singh, K. (1992). Moving blocks jackknife and bootstrap capture weak dependence. In <i>Exploring the Limits of Bootstrap</i> (R. LePage and L. Billard, eds.) 225--248. Wiley, New York.

Grenander, U. and Rosenblatt, M. (1957). <i>Statistical Analysis of Stationary Time Series</i>. Wiley, New York.

Fuller, W. A. (1996). <i>Introduction to Statistical Time Series</i>, 2nd ed. Wiley, New York.

Quenouille, M. (1949). Approximate tests of correlation in time-series. <i>J. Roy. Statist. Soc. Ser. B</i> <b>11</b> 68--84.

Wu, C.-F. J. (1986). Jackknife, bootstrap and other resampling methods in regression analysis (with discussion). <i>Ann. Statist.</i> <b>14</b> 1261--1350.

Choi, E. and Hall, P. (2000). Bootstrap confidence regions computed from autoregressions of arbitrary order. <i>J. R. Stat. Soc. Ser. B Stat. Methodol.</i> <b>62</b> 461--477.

Horowitz, J. L. (2003). Bootstrap methods for Markov processes. <i>Econometrica</i> <b>71</b> 1049--1082.

Paparoditis, E. and Politis, D. N. (2003). Residual-based block bootstrap for unit root testing. <i>Econometrica</i> <b>71</b> 813--855.

Arcones, M. A. (2001). On the asymptotic accuracy of the bootstrap under arbitrary resampling size. <i>Ann. Inst. Statist. Math.</i> To appear.

Babu, G. J. and Singh, K. (1983). Inference on means using the bootstrap. <i>Ann. Statist.</i> <b>11</b> 999--1003.

Bartlett, M. S. (1946). On the theoretical specification and sampling properties of autocorrelated time-series. <i>Suppl. J. Roy. Statist. Soc.</i> <b>8</b> 27--41.

Bertail, P. and Politis, D. N. (2001). Extrapolation of subsampling distribution estimators: The i.i.d. and strong mixing cases. <i>Canad. J. Statist.</i> <b>29</b> 667--680.

Bickel, P. and Freedman, D. A. (1981). Some asymptotic theory for the bootstrap. <i>Ann. Statist.</i> <b>9</b> 1196--1217.

Bickel, P., Götze, F. and van Zwet, W. R. (1997). Resampling fewer than $n$ observations: Gains, losses, and remedies for losses. <i>Statist. Sinica</i> <b>7</b> 1--32.

Bollerslev, T., Chou, R. and Kroner, K. (1992). ARCH modelling in finance: A review of the theory and empirical evidence. <i>J. Econometrics</i> <b>52</b> 5--59.

Booth, J. G. and Hall, P. (1993). An improvement of the jackknife distribution function estimator. <i>Ann. Statist.</i> <b>21</b> 1476--1485.

Brockwell, P. and Davis, R. (1991). <i>Time Series</i>: <i>Theory and Methods</i>, 2nd ed. Springer, New York.

Bühlmann, P. (1997). Sieve bootstrap for time series. <i>Bernoulli</i> <b>3</b> 123--148.

Bühlmann, P. (2002). Bootstraps for time series. <i>Statist. Sci.</i> <b>17</b> 52--72.

Davison, A. C. and Hall, P. (1993). On Studentizing and blocking methods for implementing the bootstrap with dependent data. <i>Austral. J. Statist.</i> <b>35</b> 215--224.

Efron, B. and Tibshirani, R. J. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy (with discussion). <i>Statist. Sci.</i> <b>1</b> 54--77.

Engle, R., ed. (1995). <i>ARCH: Selected Readings</i>. Oxford Univ. Press.

Franke, J., Kreiss, J.-P. and Mammen, E. (2002). Bootstrap of kernel smoothing in nonlinear time series. <i>Bernoulli</i> <b>8</b> 1--37.

Freedman, D. A. (1984). On bootstrapping two-stage least-squares estimates in stationary linear models. <i>Ann. Statist.</i> <b>12</b> 827--842.

Giné, E. and Zinn, J. (1990). Necessary conditions for the bootstrap of the mean. <i>Ann. Statist.</i> <b>17</b> 684--691.

Götze, F. and Künsch, H. (1996). Second-order correctness of the blockwise bootstrap for stationary observations. <i>Ann. Statist.</i> <b>24</b> 1914--1933.

Granger, C. and Andersen, A. (1978). <i>An Introduction to Bilinear Time Series Models</i>. Vandenhoeck und Ruprecht, Göttingen.

Hall, P., DiCiccio, T. J. and Romano, J. P. (1989). On smoothing and the bootstrap. <i>Ann. Statist.</i> <b>17</b> 692--704.

Hamilton, J. D. (1994). <i>Time Series Analysis</i>. Princeton Univ. Press.

Härdle, W. and Bowman, A. (1988). Bootstrapping in nonparametric regression: Local adaptive smoothing and confidence bands. <i>J. Amer. Statist. Assoc.</i> <b>83</b> 102--110.

Kreiss, J.-P. (1988). Asymptotic statistical inference for a class of stochastic processes. Habilitationsschrift, Faculty of Mathematics, Univ. Hamburg, Germany.

Kreiss, J.-P. (1992). Bootstrap procedures for AR($\infty$) processes. In <i>Bootstrapping and Related Techniques</i> (K. H. Jöckel, G. Rothe and W. Sendler, eds.) 107--113. Springer, Berlin.

Lahiri, S. N. (1999). Theoretical comparisons of block bootstrap methods. <i>Ann. Statist.</i> <b>27</b> 386--404.

Masry, E. and Tjøstheim, D. (1995). Nonparametric estimation and identification of nonlinear ARCH time series. <i>Econometric Theory</i> <b>11</b> 258--289.

Neumann, M. and Kreiss, J.-P. (1998). Regression-type inference in nonparametric autoregression. <i>Ann. Statist.</i> <b>26</b> 1570--1613.

Paparoditis, E. (1992). Bootstrapping some statistics useful in identifying ARMA models. In <i>Bootstrapping and Related Techniques</i> (K. H. Jöckel, G. Rothe and W. Sendler, eds.) 115--119. Springer, Berlin.

Paparoditis, E. and Politis, D. N. (2000). The local bootstrap for kernel estimators under general dependence conditions. <i>Ann. Inst. Statist. Math.</i> <b>52</b> 139--159.

Paparoditis, E. and Politis, D. N. (2001a). Tapered block bootstrap. <i>Biometrika</i> <b>88</b> 1105--1119.

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

Paparoditis, E. and Politis, D. N. (2001c). The continuous-path block-bootstrap. In <i>Asymptotics in Statistics and Probability</i> (M. Puri, ed.) 305--320. VSP Publications, Zeist, The Netherlands.

Paparoditis, E. and Politis, D. N. (2002a). The local bootstrap for Markov processes. <i>J. Statist. Plann. Inference</i> <b>108</b> 301--328.

Paparoditis, E. and Politis, D. N. (2002b). The tapered block bootstrap for general statistics from stationary sequences. <i>Econom. J.</i> <b>5</b> 131--148.

Paparoditis, E. and Politis, D. N. (2002c). Local block bootstrap. <i>C. R. Math. Acad. Sci. Paris.</i> <b>335</b> 959--962.

Politis, D. N. (2001a). Resampling time series with seasonal components. In <i>Frontiers in Data Mining and Bioinformatics</i>: <i>Proceedings of the 33rd Symposium on the Interface of Computing Science and Statistics</i>.

Politis, D. N. (2001b). Adaptive bandwidth choice. <i>J. Nonparametr. Statist.</i> To appear.

Politis, D. N. and Romano, J. P. (1992a). A general resampling scheme for triangular arrays of $\alpha$-mixing random variables with application to the problem of spectral density estimation. <i>Ann. Statist.</i> <b>20</b> 1985--2007.

Politis, D. N. and Romano, J. P. (1992b). A circular block-resampling procedure for stationary data. In <i>Exploring the Limits of Bootstrap</i> (R. LePage and L. Billard, eds.) 263--270. Wiley, New York.

Politis, D. N. and Romano, J. P. (1992c). A general theory for large sample confidence regions based on subsamples under minimal assumptions. Technical Report 399, Dept. Statistics, Stanford Univ.

Politis, D. N. and Romano, J. P. (1993). Estimating the distribution of a Studentized statistic by subsampling. <i>Bull. Internat. Statist. Inst.</i> <b>2</b> 315--316.

Politis, D. N. and Romano, J. P. (1994a). The stationary bootstrap. <i>J. Amer. Statist. Assoc.</i> <b>89</b> 1303--1313.

Politis, D. N. and Romano, J. P. (1994b). Large sample confidence regions based on subsamples under minimal assumptions. <i>Ann. Statist.</i> <b>22</b> 2031--2050.

Politis, D. N. and White, H. (2001). Automatic block-length selection for the dependent bootstrap. <i>Econometric Rev.</i> To appear.

Quenouille, M. (1956). Notes on bias in estimation. <i>Biometrika</i> <b>43</b> 353--360.

Radulovic, D. (1996). The bootstrap of the mean for strong mixing sequences under minimal conditions. <i>Statist. Probab. Lett.</i> <b>28</b> 65--72.

Rajarshi, M. B. (1990). Bootstrap in Markov sequences based on estimates of transition density. <i>Ann. Inst. Statist. Math.</i> <b>42</b> 253--268.

Romano, J. P. and Thombs, L. (1996). Inference for autocorrelations under weak assumptions. <i>J. Amer. Statist. Assoc.</i> <b>91</b> 590--600.

Sakov, A. and Bickel, P. (2000). An Edgeworth expansion for the $m$ out of $n$ bootstrapped median. <i>Statist. Probab. Lett.</i> <b>49</b> 217--223.

Shao, J. and Wu, C.-F. J. (1989). A general theory for jackknife variance estimation. <i>Ann. Statist.</i> <b>17</b> 1176--1197.

Sherman, M. and Carlstein, E. (1996). Replicate histograms. <i>J. Amer. Statist. Assoc.</i> <b>91</b> 566--576.

Shi, S. G. (1991). Local bootstrap. <i>Ann. Inst. Statist. Math.</i> <b>43</b> 667--676.

Subba Rao, T. and Gabr, M. (1984). <i>An Introduction to Bispectral Analysis and Bilinear Time Series Models</i>. <i>Lecture Notes in Statist.</i> <b>24</b>. Springer, New York.

Swanepoel, J. W. H. (1986). A note on proving that the (modified) bootstrap works. <i>Comm. Statist. Theory Methods</i>. <b>15</b> 3193--3203.

Swanepoel, J. W. H. and van Wyk, J. W. J. (1986). The bootstrap applied to power spectral density function estimation. <i>Biometrika</i> <b>73</b> 135--141.

Tong, H. (1990). <i>Non-linear Time Series</i>: <i>A Dynamical Systems Approach</i>. Oxford Univ. Press.

Tukey, J. W. (1958). Bias and confidence in not-quite large samples (abstract). <i>Ann. Math. Statist.</i> <b>29</b> 614.