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References
Abramovitch, L. and Singh, K. (1985). Edgeworth corrected pivotal statistics and the bootstrap. Ann. Statist 13 116–132.
Araujo, A. and Giné, E. (1980). The central limit theorem for real and banach valued random variables Wiley, New York.
Arcones, M. A. and Giné, E. (1990). Some bootstrap tests of symmetry for univariate continous distributions. Ann. Statist 19 1496–1511.
Athreya, K.B. (1987). Bootstrap of the mean in the infinite variance case. Ann. Statist 15 724–731.
Azzalini, A., Bowman, A.W., and Hardie, W. (1989). On the use of nonparametric regression for model checking. Biometrika 76 1–11.
Babu, G. J. and Singh, K. (1984). On one - term Edgeworth correction by Efron’s bootstrap. Sankhya, Ser A 46 219–232.
Basawa, I. V., Mallik, A. K., McCormick, W. P., Reeves, J. H., and Taylor, R. L. (1991). Bootstrapping unstable first - order autoregressive processes. Ann. Statist 19 1098–1101.
Basawa, I. V., Mallik, A. K., McCormick, W. P., and Taylor, R. L. (1989). Bootstrapping explosive autoregressive processes. Ann. Statist 17 1479–1486.
Beran, R. (1982). Estimated sampling distributions: The bootstrap and competitors. Ann. Statist 10 212–225.
Beran, R. (1984a). Jackknife approximation to bootstrap estimates. Ann. Statist 12 101–118.
Beran, R. (1984b). Bootstrap methods in statistics. Jahresber. Math. Ver 86 14–30.
Beran, R. (1986). Discussion to Wu,C.F.J.: Jackknife, bootstrap and other resampling methods in regression analysis. Ann. Statist 14 1295–1298.
Beran, R. (1987). Prepivoting to reduce level error of confidence sets. Biometrika 74 457–468.
Beran, R. (1988). Prepivoting test statistics: A bootstrap view of asymptotic refinements. J. Amer. Statist. Assoc 83 687–697.
Beran, R. and Ducharme, G. R. (1991). Asymptotic Theory for Bootstrap Methods in Statistics Publications CRM, Universite de Montreal
Beran, R. and Srivastava, M. S. (1985). Bootstrap tests and confidence regions for functions of a covariance matrix. Ann. Statist. 13 95–115. [Correction: 15 (1987) 470–471.]
Bhattacharya, R. (1987). Some aspects of Edgeworth expansions in statistics and probability. In New Perspectives in Theoretical and Applied Statistics (M. L. Puri, J. P. Vilaplana and W. Wertz, eds.) 157–171 Wiley, New York.
Bhattacharya, R. and Qumsiyeh, M. (1989). Second order and LP - comparisons between the bootstrap and empirical Edgeworth expansion methologies. Ann. Statist 17 160–169.
Bickel, P. J. and Freedman, D. A. (1980). On Edgeworth expansions for the bootstrap. Technical report Department of Statistics, University of California, Berkeley.
Bickel, P. J. and Freedman, D. A. (1981). Some asymptotic theory for the bootstrap. Ann. Statist 9 1196–1217.
Bickel, P. J. and Freedman, D. A. (1983). Bootstrapping regression models with many parameters. A Festschrift for Erich Lehmann (P. Bickel, K. Doksum, and J. C. Hodges, eds.) 28–48. Wadsworth, Belmont, California.
Bickel, P. J. and Rosenblatt, M. (1973). On some global measures of the deviations of density function estimates. Ann. Statist. 1 1071–1095. [Correction: 3 (1975) 1370.]
Billingsley, P. (1968). Convergence of probability measures Wiley, New York.
Bose, A. (1988). Edgeworth correction by bootstrap in autoregressions. Ann. Statist 16 1709–1722.
Bretagnolle, J. (1983). Lois limites du bootstrap de certaines fonctionelles. Ann. Inst. H. Poincaré, Sec. B 19 281–296.
Cao-Abad, R. (1991). Rate of convergence for the wild bootstrap in nonparametric regression. Ann. Statist 19 2226–2231.
Cao-Abad, R. and Gonzales - Manteiga, W. (1990). Bootstrap methods in regression smoothing: an alternative procedure to the wild resampling plan. Unpublished manuscript
Carroll, R. J. (1982). Adapting for Heteroscedasticity in Linear Models. Ann. Statist 10 1224–1233.
Collomb, G. and Hardie, W. (1986). Strong Uniform Convergence Rates in Robust Nonparametric Time Series Analysis and Prediction; Kernel Regression Estimation from Dependent Observations. Stochastic Processes and their Applications 23 77–89.
Cox, D., Koh, E., Wahba, G. and Yandell, B. (1988). Testing the (parametric) null model hypothesis in (semiparametric) partial and generalized spline models. Ann. of Statist 16 113–119.
Cox, D. and Koh, E. (1989). A smoothing spline based test of model adequacy in polynomial regression. Ann. Inst. Statist. Math 41 383–400.
Cramér, H. and Leadbetter, M. R. (1967). Stationary and Related Processes Wiley, New York, London, Sydney.
Csörgö, S. and Mason, D. M. (1989). Bootstrapping empirical functions. Ann. Statist 17 1447–1471.
Cuevas, A. and Gonzalez Manteiga, W. (1990). Data - driven smoothing based on convexity properties. Unpublished manuscript
Cuevas, A. and Gonzalez Manteiga, W. (1991). Data - driven smoothing based on convexity properties. In Nonparametric Functional Estimation and Related Topics. Proceedings of the NATO Advanced Study Institute, Spetses (Greece) (G. Roussas ed.) 225–240. Kluver, Dordrecht.
Davies, L. (1990). The asymptotics of S - estimators in the linear regression model. Ann. Statist 18 1651–1675.
Diciccio, T. J. and Efron, B. (1990). Better approximate confidence intervals in exponential families. Technical report, Stanford University.
Diciccio, T. J. and Romano, J. P.(1988a). A review of bootstrap confidence intervals (with discussion). J. R Statist. B 50 338–354.
Diciccio, T. J. and Romano, J. P. (1988b). On parametric bootstrap procedures for second - order accurate confidence limits. Technical report, Stanford University.
Diciccio, T. J. and Tibshirani, R. (1987). Bootstrap confidence intervals and bootstrap approximations. J. Amer. Statist. Assoc 82 163–170.
Dikta, G. (1988). Approximation of nearest neighbour regression function estimators. Technical report, University of Gießen
Do, K. - A. and Hall, P. (1991). On importance resampling for the bootstrap. Biometrika 78 161–167.
Dunford, N. and Schwartz, J. (1985). Linear operators Interscience 1, New York.
Dümbgen, L. (1991a). The asymptotic behaviour of some nonparametric change - point estimators. Ann. of Statist 19 1471–1496.
Dümbgen, L. (199lb). On nondifferentiable functions and the bootstrap. Preprint
Efron, B.(1979). Bootstrap methods:Another look at the Jackknife Ann. of Statist 7 1–26.
Efron, B. (1982). The jackknife, the bootstrap and other resampling plans CBMS-NSF Regional Conference Series in Applied Mathem. Monogr 38. SIAM, Philadelphia.
Efron, B. (1987). Better bootstrap confidence intervals. (with discussion) J. Amer. Statist. Assoc 82 171–200.
Efron, B. (1990). More efficient bootstrap computations. J. Amer. Statist. Assoc 85 79–89.
Efron, B. and Gong, G. (1983). A leisurely look at the booktstrap, the jackknife and croosvalidation. Amer. Statistician 37 36–48
Efron, B. and Stein, C. (1981). The jackknife estimate of variance. Ann. Statist 9 586–596.
Efron, B. and Tibshirani, R. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statist. Science 1 54–77.
Ehm, W. (1986). On maximum likelihood estimation in high - dimensional log - linear type models. I. The independent case. Preprint Sonderforschungsbereich 123, Universität Heidelberg.
Ehm, W. (1991). Statistical problems with many parameters: critical quantities for approximate normality and posterior density based inference Habilitationsschrift, Universität Heidelberg.
Esseen, C. G. (1966). On the Kolmogorov-Rogozin inequality for the concentration function. Z. Wahrscheinlichkeitstheorie verw. Geb 5 210–216.
Eubank, R. and Spiegelman, C. (1989). Testing the goodness - of - fit of linear models via nonparametric regression techniques. Unpublished manuscript
Falk, M. and Kaufmann, E. (1991). Coverage probabilities of bootstrap - confidence intervals for quantiles. Ann. Statist. 19 485–495.
Falk, M. and Reiss, R. - D. (1989a). Weak convergence of smoothed and nonsmoothed bootstrap quantile estimates. Ann. Probab 17 362–371.
Falk, M. and Reiss, R. - D. (1989b). Bootstrapping the distance between smooth bootstrap and sample quantile distribution. Probab. Theory Rel. Fields 82 177-186.
Feller, W. (1966). An introduction to probability theory and its applications. Vol. II. Wiley, New York.
Firth, D., Glosup, J., and Hinkley, D.V. (1989). Nonparametric curves for checking model fit. Unpublished manuscript
Fisher, N.I., Mammen, E. and Marron, J.S. (1990). Testing for multimodality. Preprint Sonderforschungsbereich 123, Universität Heidelberg.
Franke, J.(1990). Bootstrapping of nonlinear autoregressive time series Some preliminary remarks. Proceedings of the Bootstrap Conference in Trier, Germany Springer, Heidelberg. To appear.
Franke, J. and Hardie, W. (1990). On bootstrapping kernel spectral estimates. Unpublished manuscript
Freedman, D.A. (1981). Bootstrapping regression models. Ann of Statist 9 1218–1228.
Ghosh, B. K. and Huang, W. - M. (1991). The power and optimal kernel of the Bickel - Rosenblatt test for goodness of fit. Ann. Statist 19 999–1009.
Gill, R. D. (1989). Non-and semi - parametric maximum likelihood estimators and the von Mises method (part 1). Scand. J. Statist 16 97–128.
Giné, E. and J. Zinn (1989). Necessary conditions for the bootstrap of the mean. Ann. Statist 17 684–691.
Giné, E. and J. Zinn (1990). Bootstrapping general empirical measures. Ann. Prob. 18
Gnedenko, B. V. and Kolmogorov, A. N. (1954). Limit distributions for sums of independent random variables. Addison - Wesley, Reading, Massachusetts.
Good, I. J. and Gaskins, R. A. (1980). Density estimation and bump - hunting by the penalized likelihood method exemplified by scattering and meteorite data. J. Amer. Statist. Assoc 75 42–73.
Götze, F. (1984). Expansions for von Mises functionals. Z. Wahrsch. verw. Gebiete 65 599–625.
Götze, F. (1985). Asymptotic expansions in functional limit theorems. J. Multivariate Anal 16 1–20.
Götze, F. (1989). Edgeworth expansions in functional limit theorems. Ann. Probab 17 1602–1634.
Haberman, S. J. (1977a). Log - linear and frequency tables with small expected cell counts. Ann. Statist 5 1148–1169.
Haberman, S. J. (1977b), Maximum likelihood estimates in exponential response models. Ann. Statist. 5 815–841
Hall, P. (1984). Central limit theorems for integrated square error of multivariate nonparametric density estimators. J. Multivar. Anal 14 1–16.
Hall, P. (1986a). On the bootstrap and confidence intervals. Ann. Statist 14 1431–1452.
Hall, P. (1986b). On the number of bootstrap simulations required to construct a confidence interval. Ann. Statist 14 1453–1462.
Hall, P. (1988). Theoretical comparison of bootstrap confidence intervals. Ann. Statist 16 927–953.
Hall, P. (1990a). Edgeworth expansions for nonparametric density estimators, with applications. Unpublished manuscript
Hall, P. (1990b). Using the bootstrap to estimate mean squared error and select smoothing parameter in nonparametric problems. J. Multivar. Anal 32 177–203.
Hall, P. (1990c). On bootstrap confidence intervals in nonparametric regression. Unpublished manuscript
Hall, P. (1990d). Asymptotic properties of the bootstrap for heavy - tailed distributions. Ann. Probab 18 1342–1360.
Hall, P. (1991). Bahadur representations for uniform resampling and importance resampling with applications toasymptotic relative efficiency. Ann. Statist 19 1062–1072.
Hall, P. (1992). The bootstrap and Edgeworth expansions Springer, New York.
Hall, P. and Marron, J. S. (1987). Extent to which least - squares cross - validation minimises integrated squared error in nonparametric density estimation. Probab. Theory Rel. Fields 74 567–581.
Hall, P. and Marron, J. S. (1991). Lower bounds for bandwidth selection in density estimation. Probab. Theory Rel. Fields To appear.
Hall, P. Marron, J. S., Park, B.U. (1989). Smoothed cross – validation. Unpublished manuscript.
Hall, P. and Martin, M. A. (1988a). Exact convergence rate of bootstrap quantile variance estimator. Probab. Theory Rel. Fields 80 261–269.
Hall, P. and Martin, M. A. (1988b). On bootstrap resampling and iteration. Biometrika 75 661–671.
Hall, P., Sheather, S. J., Jones, M. C. and Marron, J. S. (1991). On optimal data-based bandwidth selection in kernel density estimation. Biometrika 78 263–269.
Hall, P. and Titterington, D. M. (1989). The effect of simulation order on level accuracy and power of Monte Carlo tests. J. R. Statist. Soc. B 51 459–467.
Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J. and Stahel, W. A. (1986). Robust statistics. The approach based on influence functions Wiley, New York.
Härdle, W. (1990). Applied nonparametric regression Econometric Society Monograph Series, Cambridge University Press.
Härdle, W. and Bowman, A. (1988). Bootstrapping in nonparametric regression: local adaptive smoothing and confidence bands. J. Amer. Statist. Assoc 83 102–110.
Hardie, W. and Mammen, E. (1990). Comparing non parametric versus parametric regression fits. Preprint.Sonderforschungsbereich 123, Universität Heidelberg.
Härdle, W. and Mammen, E. (1991). Bootstrap methods in nonparametric regression. In Nonparametric Functional Estimation and Related Topics. Proceedings of the NATO Advanced Study Institute, Spetses (Greece) (G. Roussas ed.) 111–124. Kluver, Dordrecht.
Härdle, W. and Marron, J. S. (1990). Semiparametric comparison of regression curves. Ann. Statist 18 63–89.
Härdle, W. and Marron, J. S. (1991). Bootstrap simultaneous error bars for nonparametric regression. Ann. Statist 19 778–796.
Hartigan, J. A. and Hartigan, P. M. (1985). The DIP test of unimodality. Ann Statist 13 70–84.
Hartigan, J. A. (1986). Comment to Efron, B. and Tibshirani, R.: Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statist. Science 1 5-76.
Helmers, R. (1991a). On the Edgeworth expansion and the bootstrap approximation for a studentised U - statistic. Ann. Statist 19 470–484.
Helmers, R. (1991b). Bootstrap methods. Unpublished book manuscript.
Hinkley, D. V. (1988). Bootstrap methods (with discussion). J. R. Statist. B 50 321–337.
Huber, P. J. (1981). Robust statistics. Wiley, New York.
Janas, D. (1991). A smoothed bootstrap estimator for a studentized sample quantile. Preprint. Sonderforschungsbereich 123, Universität Heidelberg.
Jeganathan, P. (1990). On Edgeworth expansions for estimators in time series models. I. Nonlinear regression models. Unpublished manuscript
Joeckel, K. H., Rothe, G., and Sendler, W. (eds.) (1992). Bootstrapping and related techniques. Lecture Notes in Econometrics and Math. Systems Vol. 376. Springer, New York.
Johns, M. V. (1988). Importance sampling for bootstrap confidence intervals. J. Amer. Statist. Assoc. 83 709–714.
Jong, P. de (1987). A central limit theorem for generalized quadratic forms. Probab. Th. Rel. Fields 75 261–277.
Karlin, S. and Rinott, Y. (1982). Applications of ANOVA type decompositions of conditional variance statistics including Jackknife estimates. Ann. Statist 10 485–501.
Kantorowitsch, L. W. and Akilow, G. P. (1964). Funktionalanalysis in normierten Räumen.. Akademie Verlag, Berlin.
Knight, K. (1989). On the bootstrap of the sample mean in the infinite variance case. Ann. Statist 17 1168–1175.
Koehler, K. J. (1986). Goodness - of - fit tests for log-linear models in sparse contingency tables. J. Amer. Statist. Assoc 81 483–493.
Kreiss, J. - P. (1988). Bootstrap procedures for AR - processes. Unpublished manuscript
Künsch, H. R. (1989). The jackknife and the bootstrap for general stationary observations. Ann. Statist 17 1217–1241.
Le Cam, L. (1986). Asymptotic methods in statistical decision theory Springer, New York.
LePage, R. and Billard, L. (eds.) (1992). Exploring the limits of bootstrap Wiley, New York.
Liu, R. (1988). Bootstrap procedures under some non i.i.d. models. Ann. Stat 16 1696–1708.
Liu, R. and Singh K. (1987). On a partial correction by the bootstrap. Ann. Stat 15 1713–1718.
Liu, R. and Singh, K. (1989). Efficiency and robustness in resampling. Unpublished manuscript
Liu, R. and Singh, K. (1991). Using i.i.d. bootstrap for general non - i.i.d. models. Unpublished manuscript
Mack, Y.P. and Silverman, B.W. (1982). Weak and strong uniform consistency of kernel regression estimates. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete 61 405–415.
Mammen, E. (1989a). Asymptotics with increasing dimension for robust regression with applications to the bootstrap. Ann. Statist 17 382–400.
Mammen, E. (1989b). Bootstrap and wild bootstrap for high - dimensional linear models. Preprint Sonderforschungsbereich 123, Universität Heidelberg.
Mammen, E. (1990) Higher – order accuracy of bootstrap for smooth functionals. Preprint Sonderforschungsbereich 123, Universität Heidelberg.
Mammen, E. (1991a). Estimating a smooth monotone regression function. Ann. Statist 19 724–740.
Mammen, E. (1991b). Nonparametric regression under qualitative smoothness assumptions. Ann. Statist 19 741–759.
Mammen, E. (1991c). Nonparametric curve estimation and simple curve characteristics. In Nonparametric Functional Estimation and Related Topics. Proceedings of the NATO Advanced Study Institute, Spetses (Greece) (G. Roussas ed.) 133–140. Kluver, Dordrecht.
Mammen, E. (1991d). On qualitative smoothness of kernel density estimates. Preprint Sonderforschungsbereich 123, Universität Heidelberg.
Mammen, E. (1992). Bootstrap, wild bootstrap, and asymptotic normality. Preprint Sonderforschungsbereich 123, Universität Heidelberg.
Mammen, E., Marron, J. S. and Fisher,N. I. (1992). Some asymptotics for multimodality tests based on kernel density estimates. Probab. Theory Rel. Fields In press.
Martin, M. A. (1990). On bootstrap iteration for coverage correction in confidence intervals. J. Statist. Amer. Assoc 85 1105–1118.
McDonald, J. A. (1982). Projection pursuit regression with the ORION I workstation. 20 minute, 16 mm color sound film Stanford University.
Müller, D.W. and Sawitzki, G. (1990). Using excess mass estimates to investigate the modality of a distribution. Proceedings of the ICOSCO - I Conference (First International Conference on Statistical Computing, Çesme,Izmir, Turkey, 1987). Ed. by E. C. van der Meulen
Müller, D. W. and Sawitzki, G. (1991). Excess mass estimates and tests for multi-modality. J. Amer. Statist. Assoc 86 738–746.
Nadaraya, E.A. (1964). On estimating regression. Theory Prob. Appl 10 186–190.
Navidi, W. (1989). Edgeworth expansions for bootstrapping regression models. Ann. Sstaist. 17 1472–1478
Neuhaus, G. (1986). A class of quadratic goodness - of - fit tests. Unpublished manuscript.
Neuhaus, G. (1988). Addendum to “Local Asymptotics for Linear Rank Statistics with estimated Score Functions”. Ann. Statist. 16 1342–1343.
Park, B.U. and Marron, J.S. (1990). Comparison of data - driven bandwidth selectors. J. Amer. Statist. Assoc. 85 66.
Parr, W. C. (1985). Jackknifing differentiable statistical functionals. J. Roy. Statist. Soc. Ser. B 47 56–66.
Pflanzagl, J. and Wefelmeyer W. (1985). Asymptotic expansions for general statistical models Lecture Notes in Statistics 31, Springer, Berlin, Heidelberg.
Portnoy, S. (1984). Asymptotic behaviour of M-estimators of p regression parameters when is large. I. Consistency. Ann. Statist 12 1298–1309.
Portnoy, S. (1985). Asymptotic behaviour of M-estimator of p regression parameters when is large. II. Normal approximation. Ann. Statist. 13 1403–1417. [Correction: 19 (1991) 2282.]
Portnoy, S. (1988). Asymptotic behaviour of likelihood methods for exponential families when the number of parameters tends to infinity. Ann. Statist. 16 356–366.
Raikov, D. A. (1938). On a connection between the central limit theoremin the theory of probability and the law of large numbers. Izvestiya Akad. Nauk SSR, Ser. Mat 323–338.
Raz, J. (1990). Testing for no effect when estimating a smooth function by nonparametric regression: a randomization approach. J. Amer. Statist. Assoc. 85 132–138.
Reeds, J. A. (1976). On the definition of von Mises functionals. Thesis. Harvard University. Cambridge, Massachusetts.
Romano, J. H. (1988). A bootstrap revival of some non - parametric distance tests. J. Amer. Statist. Assoc. 83 698–708.
Romano, J. H. (1989). Bootstrap and randomization tests of some nonparametric hypotheses. Ann Statist 14 141–159.
Rousseeuw, P. J. and Yohai, V. J. (1984). Robust regression by means of S-estimators. Robust and Nonlinear Time Series Analysis (J. Franke, Hardie, W. and Martin, D., eds.). Lecture Notes in Statistics 26 256–272. Springer, New York.
Sauermann, W. (1986). Bootstrap - Verfahren in log - linearen Modellen. Dissertation. Universität Heidelberg.
Sauermann, W. (1989). Bootstrapping the maximum likelihood estimator in high-dimensional log-linear models. Ann. Statist 17 1198–1216.
Sen, P. K. (1988a). Functional jackknifing: Rationality and general asymptotics. Ann. Statist 16 450–469.
Sen, P. K. (1988b). Functional approaches in resampling plans: a review of some recent developments. Sankhya 50 394–435.
Shao, J. (1990). Bootstrap estimation of the asymptotic variances of statistical functionals. Ann. Inst. Statist. Math 42 737–752.
Shao, J. and Wu, C. F. J. (1989). A general theory for jackknife variance estimation. Ann. Statist 17 1176–1197.
Shorack, G. (1982). Bootstrapping robust regression. Comm. Statist A 11 961–972.
Silverman, B. W. (1978). Weak and strong uniform consistency of the kernel estimate of a density and its derivatives. Ann. Statist 6 177–184.
Silverman, B. W. (1981). Using kernel estimates to investigate multimodality. Journal of the Royal Statistical Society, Series B 43 97–99.
Silverman, B. W. (1983). Some properties of a test for multimodality based on kernel density estimates. Probability, Statistics and Analysis (Kingman, J.F.C. and Reuter, G.E.H.,eds.) 248–259. Cambridge University Press, Cambridge.
Silverman, B. W. (1986). Density estimation for statistics and data analysis. Chapmen and Hall, London.
Singh, K. (1981). On the asymptotic accuracy of Efron’s bootstrap. Ann. Statist 9 1187–1195.
van Zwet, W. R. (1984). A Berry - Esséen bound for symmetric statistics. Z. Wahrsch. Verw. Gebiete 66 425–440.
van Zwet, W. (1989). Hoeffding’s decomposition and the bootstrap. Talk given at the conference on “Asymptotic methods for computer-intensive procedures in statistics” in Oberwolfach, West-Germany.
Watson, G. S. (1964). Smooth regression analysis. Sankhya,Series A 359 372
Wu, C. F. J. (1986). Jackknife, bootstrap, and other resampling methods in regression analysis (with discussion). Ann. Statist 14 1261–1295.
Yin, Y. Q., Bai, Z. D., and Krishnaiah, P. R. (1988). On the limit of the largest eigenvalue of the large dimensional sample covariance matrix. Probab. Th. Rel. Fields 78 509–521.
Yohai, V. J. (1987). High breakdown point and high efficiency robust estimates for regression. Ann. Statist 15 642–656.
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Mammen, E. (1992). References. In: When Does Bootstrap Work?. Lecture Notes in Statistics, vol 77. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2950-6_10
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