The Living Thing / Notebooks :

Bootstrap

Resampling your own data to estimate how good your estimator is, frequentist-style. Gets tricky for dependent data, and various other things.

For a handy crib sheet for bootstrap failure modes, see Thomas Lumley, When the bootstrap doesn’t work

Bootstrap bias correction

As opp variance estimation. When does this work? Need to understand that better.

Boostrap for dependent data

e.g., as presaged, time series.

“Wild bootstrap”

What is this even?

Pedagogic

General refs

AMBP08
Albeanu, G., Madsen, H., Burtschy, B., Popentiu-Vladicescu, F., & Ghica, M. (2008) Bootstrapping time series with application to risk management. R & RATA, Electronic Journal of International Group on Reliability, 1(3), 84–93.
Bach00
Bach, F. (n.d.) Model-Consistent Sparse Estimation through the Bootstrap.
BeKi00
Berkowitz, J., & Kilian, L. (2000) Recent developments in bootstrapping time series. Econometric Reviews, 19(1), 1–48. DOI.
Biew02
Biewen, M. (2002) Bootstrap inference for inequality, mobility and poverty measurement. Journal of Econometrics, 108(2), 317–342. DOI.
Bühl02
Bühlmann, P. (2002) Bootstraps for Time Series. Statistical Science, 17(1), 52–72.
BüKü99
Bühlmann, P., & Künsch, H. R.(1999) Block length selection in the bootstrap for time series. Computational Statistics & Data Analysis, 31(3), 295–310. DOI.
Came13
Cameron, A. C.(2013) Inference for Health Econometrics: Inference, Model Tests, Diagnostics, Multiple Tests, and Bootstrap.
ChHa15
Chang, J., & Hall, P. (2015) Double-bootstrap methods that use a single double-bootstrap simulation. Biometrika, 102(1), 203–214. DOI.
ChLo97
Chen, K., & Lo, S.-H. (1997) On a mapping approach to investigating the bootstrap accuracy. Probability Theory and Related Fields, 107(2), 197–217. DOI.
CoZa10
Cogneau, P., & Zakamouline, V. (2010) Bootstrap methods for finance: Review and Analysis. . Working Paper, University of Agder
Dahl11
Dahlhaus, R. (2011) Discussion: Bootstrap methods for dependent data: A review. Journal of the Korean Statistical Society, 40(4), 379–381. DOI.
DeWe06
Dette, H., & Weißbach, R. (2006) A bootstrap test for the comparison of nonlinear time series-with application to interest rate modelling. . Technical Report/Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen
DiEf96a
DiCiccio, T. J., & Efron, B. (1996a) Bootstrap Confidence Intervals. Statistical Science, 11(3), 189–212. DOI.
DiEf96b
DiCiccio, T. J., & Efron, B. (1996b) [Bootstrap Confidence Intervals]: Rejoinder. Statistical Science, 11(3), 223–228.
Efro79
Efron, B. (1979) Bootstrap methods: another look at the jackknife. The Annals of Statistics, 7(1), 1–26. DOI.
Efro81
Efron, B. (1981) Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods. Biometrika, 68(3), 589–599. DOI.
Efro12
Efron, B. (2012) Bayesian inference and the parametric bootstrap. The Annals of Applied Statistics, 6(4), 1971–1997. DOI.
GoPo11
Gonçalves, S., & Politis, D. (2011) Discussion: Bootstrap methods for dependent data: A review. Journal of the Korean Statistical Society, 40(4), 383–386. DOI.
GoWh04
Gonçalves, S., & White, H. (2004) Maximum likelihood and the bootstrap for nonlinear dynamic models. Journal of Econometrics, 119(1), 199–219. DOI.
GöKü96
Götze, F., & Künsch, H. R.(1996) Second-order correctness of the blockwise bootstrap for stationary observations. The Annals of Statistics, 24(5), 1914–1933. DOI.
Hall92
Hall, P. (1992) On Bootstrap Confidence Intervals in Nonparametric Regression. The Annals of Statistics, 20(2), 695–711.
Hall94
Hall, P. (1994) Chapter 39 Methodology and theory for the bootstrap. In B.-H. of Econometrics (Ed.), (Vol. 4, pp. 2341–2381). Elsevier
HaHJ95
Hall, P., Horowitz, J. L., & Jing, B.-Y. (1995) On blocking rules for the bootstrap with dependent data. Biometrika, 82(3), 561–574. DOI.
HKMR92
Hanson, B., Klink, K., Matsuura, K., Robeson, S. M., & Willmott, C. J.(1992) Vector Correlation: Review, Exposition, and Geographic Application. Annals of the Association of American Geographers, 82(1), 103–116. DOI.
HäHK03
Härdle, W., Horowitz, J., & Kreiss, J.-P. (2003) Bootstrap methods for time series. International Statistical Review, 71(2), 435–459.
HaGe09
Harrison, M., & Geman, S. (2009) A Rate and History-Preserving Resampling Algorithm for Neural Spike Trains. Neural Computation, 21(5), 1244–1258. DOI.
HaAK13
Harrison, M. T., Amarasingham, A., & Kass, R. E.(2013) Statistical Identification of Synchronous Spiking. In P. M. DiLorenzo & J. D. Victor (Eds.), Spike Timing: Mechanisms and Function. CRC Press
Hest11
Hesterberg, T. (2011) Bootstrap. Wiley Interdisciplinary Reviews: Computational Statistics, 3(6), 497–526. DOI.
Hink97
Hinkley, D. V.(1997) Bootstrap methods and their application. . Cambridge ; New York, NY, USA: Cambridge University Press
HoWh05
Hong, Y., & White, H. (2005) Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence. Econometrica, 73(3), 837–901. DOI.
KrPa11
Kreiss, J.-P., & Paparoditis, E. (2011) Bootstrap methods for dependent data: A review. Journal of the Korean Statistical Society, 40(4), 357–378. DOI.
Küns89
Künsch, H. R.(1989) The Jackknife and the Bootstrap for General Stationary Observations. The Annals of Statistics, 17(3), 1217–1241.
Lahi93
Lahiri, S. N.(1993) On the moving block bootstrap under long range dependence. Statistics & Probability Letters, 18(5), 405–413. DOI.
Lahi01
Lahiri, S. N.(2001) Effects of block lengths on the validity of block resampling methods. Probability Theory and Related Fields, 121, 73–97. DOI.
Lahi03
Lahiri, S. N.(2003) Resampling methods for dependent data. . New York: Springer
LaHM15
Larivière, V., Haustein, S., & Mongeon, P. (2015) The Oligopoly of Academic Publishers in the Digital Era. PLoS ONE, 10(6), e0127502. DOI.
LeYo96
Lee, S. M. S., & Young, G. A.(1996) [Bootstrap Confidence Intervals]: Comment. Statistical Science, 11(3), 221–223.
PaSa14
Paparoditis, E., & Sapatinas, T. (2014) Bootstrap-based testing for functional data. arXiv:1409.4317 [Math, Stat].
Poli03
Politis, D. N.(2003) The Impact of Bootstrap Methods on Time Series Analysis. Statistical Science, 18(2), 219–230. DOI.
PoRo94
Politis, D. N., & Romano, J. P.(1994) The Stationary Bootstrap. Journal of the American Statistical Association, 89(428), 1303–1313. DOI.
PoWh04
Politis, D. N., & White, H. (2004) Automatic Block-Length Selection for the Dependent Bootstrap. Econometric Reviews, 23(1), 53–70. DOI.
RoRu09
Rodriguez, A., & Ruiz, E. (2009) Bootstrap prediction intervals in state–space models. Journal of Time Series Analysis, 30(2), 167–178. DOI.
RuPa02
Ruiz, E., & Pascual, L. (2002) Bootstrapping financial time series. Journal of Economic Surveys, 16(3), 271–300.
Shal10
Shalizi, C. R.(2010) The Bootstrap. American Scientist, 98(3), 186. DOI.
Shao96
Shao, J. (1996) Bootstrap Model Selection. Journal of the American Statistical Association, 91(434), 655–665. DOI.
Shib97
Shibata, R. (1997) Bootstrap estimate of Kullback-Leibler information for model selection. Statistica Sinica, 7, 375–394.
StJa04
Steck, H., & Jaakkola, T. S.(2004) Bias-Corrected Bootstrap and Model Uncertainty. In Advances in Neural Information Processing Systems (pp. 521–528). MIT Press
Ston77
Stone, M. (1977) An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike’s Criterion. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 44–47.
TRTW15
Tibshirani, R. J., Rinaldo, A., Tibshirani, R., & Wasserman, L. (2015) Uniform Asymptotic Inference and the Bootstrap After Model Selection. arXiv:1506.06266 [Math, Stat].
Vino13
Vinod, H. D.(2013) Maximum Entropy Bootstrap Algorithm Enhancements. Available at SSRN 2285041.
ViLó09
Vinod, H. D., & López-de-Lacalle, J. (2009) Maximum entropy bootstrap for time series: the meboot R package. Journal of Statistical Software, 29(5), 1–19.
VoSh96
Vogel, R. M., & Shallcross, A. L.(1996) The moving blocks bootstrap versus parametric time series models. Water Resources Research, 32(6), 1875–1882. DOI.
YaHa06
Yatchew, A., & Hardle, W. (2006) Nonparametric state price density estimation using constrained least squares and the bootstrap. Journal of Econometrics, 133(2), 579–599.