Guidelines for selecting among different types of bootstraps

References

• Barber JA, Thomson SG. Analysis cost data in randomized trials: an application of the non-parametric bootstrap. Stat Med 2000;19:3219–36
   PubMed Web of Science ®Google Scholar
• Lord J, Asante MA. Estimating uncertainty ranges for costs by the bootstrap procedure combined with probabilistic sensitivity analysis. Health Econ 1999;8:323–33
   PubMed Web of Science ®Google Scholar
• Briggs AH, Worderling DE, Mooney CZ. Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation. Health Econ 1998;6:327–40
   Google Scholar
• Neymark N, Lianes P, Smith EF, et al. Economic evaluation of three two-drug chemotherapy regimens in advanced non-small-cell lung cancer. Pharmacoeconomics 2005;23:1155–66
   PubMed Web of Science ®Google Scholar
• Obenchain RL, Robinson RL, Swindle RW. Cost effectiveness inferences from bootstrap quadrant confidence levels: three degrees of dominance. J Biopharm Stat 2005;15:419–36
   Google Scholar
• Aegerter P, Muller F, Nakache JP, et al. Evaluation of screening methods for Down’s syndrome using bootstrap comparison of ROC curves. Comput Methods Prog Biomed 1994;43:151–7
   PubMed Web of Science ®Google Scholar
• Baker SG, Chu KC. Evaluating screening for the early detection and treatment of cancer without using a randomized control group. J Am Statist Assoc 1990;85:321–7
   Web of Science ®Google Scholar
• Shen CF, Iglewicz B. Robust and bootstrap testing procedures for bioequivalence. J Bipharmacol Stat 1994;4:65–90
   PubMedGoogle Scholar
• Tsodikov A, Hasenclever D, Loeffler M. Regression with bounded outcome score: estimation of power by bootstrap and simulation in a chronic myelogenous leukemia clinical trial. Stat Med 1998;17:1909–22
   Google Scholar
• Rosen O, Cohen A. Constructing a bootstrap confidence interval for the unknown concentration in radioimmunoassay. Stat Med 1995;14;935–52
   PubMed Web of Science ®Google Scholar
• Efron B. Bootstrap methods: another look at the jackknife. Ann Statist 1979;7:1–26
   Web of Science ®Google Scholar
• Efron B. Better bootstrap confidence intervals. J Am Stat Assoc 1987;82:171–85
   Web of Science ®Google Scholar
• Lin DY, Feuer EJ, Etzioni R, et al. Estimating medical costs from incomplete follow-up data. Biometrics 1997;53:419–34
   PubMed Web of Science ®Google Scholar
• Sloan JA, Cha SS, Wagner JL, et al. Analyzing oncology patient health care costs using the SAS system. SAS Users Group International Conference 11–14 April 1999, Paper 284
   Google Scholar
• Baser O, Bradley C, Gardiner J, et al. Estimation from censored medical cost. Biom J 2004;46:351–63
   Web of Science ®Google Scholar
• Baser O, Gardiner J, Bradley C, et al. Longitudinal Analysis of Censored Medical Cost. Health Econ, 3 Jan 2006 [Epub ahead of print]
   Google Scholar
• Andrews DWK. A conditional Kolmogorov test. Econometrica 1997;65:1097–1128
   Web of Science ®Google Scholar
• Blanchard OJ, Quah D. The dynamic effects of aggregate demand and supply disturbances. Am Econ Rev 1989;79:655–73
   Web of Science ®Google Scholar
• Runkle DE. Vector autoregressions and reality. Journal of Business and Economic Statistics 1987;5:437–42
   Web of Science ®Google Scholar
• Stute W. Nonparametric model checks for regression. Ann Statist 1997;25:613–41
   Web of Science ®Google Scholar
• Horowitz JL. Bootstrap-based critical values for the information-matrix test. J Econom 1994;61:395–411
   Web of Science ®Google Scholar
• Kennan J, Neumann GR. Why does the information matrix test reject so often? Working paper no. 88–4, University of Iowa: Department of Economics, 1988
   Google Scholar
• Orme C. The small-sample performance of the information-matrix test. J Econom 1990;49:309–31
   Google Scholar
• Nelson W. Accelerated testing: statistical models, and data analyses. New York: Wiley, 1990
   Google Scholar
• Agresti A. Categorical data analysis. New York: Wiley, 1990
   Google Scholar
• Efron B. The jacknife, the bootstrap and other resampling plans. New York: Champman and Hall, 1982
   Google Scholar
• Teebagy N, Chatterjee S. Inference in a binary response model with applications to data analysis. Decision Sciences 1989;20:393–403
   Web of Science ®Google Scholar
• Lunneborg, CE. Data analysis by resampling: concepts and applications. Pacific Grove, CA: Duxbury, 2000
   Google Scholar
• Chernick MR, Friis RH. Introductory biostatistics for the health sciences. New York: Wiley, 2003
   Google Scholar
• Nelson CR, Startz R. The distribution of the instrumental variable estimator and its t ratio when the instrument is a poor one. Journal of Business 1990;63:125–40
   Web of Science ®Google Scholar
• Nelson CR, Startz R. Some further results on the exact small sample properties of the instrumental variable estimator. Econometrica 1990;58:967–76
   Web of Science ®Google Scholar
• Mammen E. When does bootstrap work. Asymptotic results and simulations. New York: Springer-verlag, 1992
   Google Scholar
• Chernick MR. Bootstrap methods: a practitioner’s guide. New York: Wiley, 1999
   Google Scholar
• Athreya KB. Bootstrap estimation of the mean in the infinite variance case. Ann Statist 1987;15:724–31
   Web of Science ®Google Scholar
• Knight K. On the bootstrap of the sample mean in the infinite variance case. Ann Statist 1989;17:1168–75
   Web of Science ®Google Scholar
• Angus JE. Asymptotic theory for bootstrapping the extremes. Commun Statist Theory Methods 1994;18:1979–82
   Google Scholar
• Liu RY. Bootstrap procedure under some non-i.i.d. models. Ann Statist 1988;16:1696–708
   Web of Science ®Google Scholar
• Wu CFJ. Jacknife bootstrap and other resampling methods in regression analysis. Ann Statist 1986;14:1261–95
   Web of Science ®Google Scholar
• Beran R. Discussion of jacknife bootstrap and other resampling methods in regression analysis by CFJ Wu. Ann Statist 1986;14:1295–8
   Web of Science ®Google Scholar
• Mammen E. Bootstrap and wild bootstrap for high dimensional linear models. Ann Statist 1993;21:255–85
   Web of Science ®Google Scholar
• Efron B, Tibshirani R. Bootstrap measures for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science 1986;1:54–77
   Google Scholar
• Weiss E. Bootstrapping with SAS. Available from http://www.winchendon.com/bootstrap.html [Accessed 09 March 2006]
   Google Scholar
• Efron B. Computer-intensive methods in statistical regression. SIAM Rev 1988;30:421–49
   Web of Science ®Google Scholar
• Hall P. On bootstrap confidence intervals in nonparametric regression. Ann Statist 1992;20:695–711
   Web of Science ®Google Scholar
• Hall P, Horowitz JL. Bootstrap critical values for tests based on generalized-method-of-moments estimators. Econometrica 1996;64:891–916
   Web of Science ®Google Scholar
• Brown B, Newey WK, May S. Efficient bootstrapping for GMM. Unpublished Manuscript. Massachusetts Institute of Technology: Department of Economics, 1997
   Google Scholar
• Mooney CZ, Duval RD. Bootstrapping: a nonparametric approach to statistical inference. London: Sage, 1993
   Google Scholar
• Efron B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman and Hall, 1993
   Google Scholar
• DiCiccio TJ, Efron B. More accurate confidence intervals in exponential families. Biometrika 1992;79:231–45
   Web of Science ®Google Scholar
• Briggs A, Wonderling D, Mooney CZ. Pulling cost-effectiveness analysis up by its bootstraps: A non-parametric approach to confidence interval estimation. Health Econ 1997;6:327–40
   Google Scholar
• Andrews DWK, Buchinsky M. On the number of bootstrap repetitions for bootstrap standard errors, confidence intervals, confidence regions, and tests, working paper. Brown University: Department of Economics, 1997
   Google Scholar
• Granger CWJ. Empirical modeling in economics: specification and evaluation. New York: Cambridge University Press, 1999
   Google Scholar
• Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika 1965;52:591–611
   Web of Science ®Google Scholar
• Shapiro SS, Francia RS. An approximate analysis of variance test for normality. J Am Stat Assoc 1967;67:215–6
   Google Scholar
• Breusch TS, Pagan AR. The LM Test and its application to model specification in econometrics. Rev Econ Stud 1980;47:239–54
   Web of Science ®Google Scholar
• White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980;48:817–38
   Web of Science ®Google Scholar
• Royston P. Estimating departure from normality. Stat Med 1991;10:1283–93
   PubMed Web of Science ®Google Scholar
• Baser O, Bradley C, Gardiner J, et al. Testing and correcting non-random selection bias due to censoring: application to medical costs. Health Services and Outcomes Research Methodology 2003;4:193–207
   Google Scholar
• Horowitz JL. Bootstrap methods in econometrics: theory and numerical performance. In: Kreps DM, Wallis KF, editors. Advances in econometrics and econometrics: theory and applications, seventh World Congress. Cambridge: Cambridge University Press, pp.188–222
   Google Scholar
• Horowitz JL. The bootstrap. In: Heckman JJ, Leamer EE, editors. Handbook of econometrics. Amsterdam: Elsevier Science, 2000, pp.3159–228
   Google Scholar
• Flachaire E. Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap. Computational Statistics and Data Analysis 2005;49:361–76
   Web of Science ®Google Scholar
• Basawa IV, Mallik AK, McCormick WP, et al. Bootstrapping unstable first-order autoregressive process. Ann Statist 1991;19:1098–101
   Web of Science ®Google Scholar
• Rayner RK. Resampling methods for tests in regression models with autocorrelated errors. Economics Letters 1991;36:281–4
   Web of Science ®Google Scholar
• Liu RY. Bootstrap procedures under some non-i.i.d. models. Ann Statist 1989;15:1713–8
   Google Scholar
• Bühlmann P. Bootstraps for time series. Stat Sci 2002;17:52–72
   Web of Science ®Google Scholar
• Härdle W, Horowitz J, Kreiss JP. Bootstrap methods for time series. Int Stat Rev 2003;71:435–59
   Web of Science ®Google Scholar
• Politis DN. The impact of bootstrap methods on time series analysis. Stat Sci 2003;18:219–30
   Web of Science ®Google Scholar
• Gray HL, Schucany WR. The generalized jacknife statistic. New York: Marcel Dekker, 1972
   Google Scholar
• Rao JNK, Wu CFJ. Resampling inference with complex survey data. J Am Statist Assoc 1988, 231–41
   Web of Science ®Google Scholar
• Rao JNK, Wu CFJ, Yue K. Some recent work on resampling methods for complex surveys. Surv Methodol 1992;209–17
   Google Scholar
• Lohr S. Sampling: Designing and analysis. Pacific Grove, CA: Duxbury Press, 1999
   Google Scholar
• Rust KF, Rao JNK. Variance estimation for complex surveys using replication techniques. Stat Methods Med Res 1999;5:283–310
   Google Scholar
• Shao H, Tu D. The jacknife and bootstrap. New York: Springer-Verlag, p.529
   Google Scholar
• Kovar JG, Rao JNK, Wu CFJ. Bootstrap and other methods to measure errors in survey estimates. Can J Statist 1988:16(Suppl):25–45
   Google Scholar
• Crown WH, Berndt ER, Baser O, et al. Benefit plan design and prescription drug utilization among asthmatics: Do patient copayment matters? Front Health Policy Res 2004;7: 95–127
   PubMedGoogle Scholar
• Manning WG, Mullahy H. Estimating log models: to transform or not to transform? J Health Econ 2001;20:461–94
   PubMed Web of Science ®Google Scholar
• Stata Reference manual. College Station: Stata Press, 2005
   Google Scholar
• Barber JA, Thompson SG, Barker J. Multiple regression of cost data: use of generalized linear models. Journal of Health Service Research and Policy 2004;4:197–204
   Google Scholar
• LePage R, Billard L. Exploring the limits of bootstrap. New York: John Wiley, 1992
   Google Scholar
• Davison AC, Hinkley DV. Bootstrap methods and their application. Cambridge: Cambridge University Press, 1997
   Google Scholar
• Hall P. Methodology and theory for the bootstrap. In: Engle RF, McFadden DF, editors. Handbook of econometrics. Amsterdam: Elsevier Science, pp. 2340–81
   Google Scholar
• Noreen E. Computer-intensive methods for testing hypothesis. New York: Wiley, 1989
   Google Scholar