boodd0.1 package

Functions for the Book "Bootstrap for Dependent Data, with an R Package"

aidedboot

Aided Frequency Bootstrap

b.star

Bootstrap Block Length Choice in the Stationary Case

bandw1

Computing the Bandwidth of the Kernel Density Estimators.

best.block.sub.size

Optimal Block Subsampling Size

best.sub.size.iid

Optimal Block Subsampling or MOON Bootstrap Sizes for I.I.D. Data

block.sub

Block Subsampling

blockboot

Block Bootstrap

blockboot.seasonal

Generalized Seasonal Block Bootstrap for Time Series.

boodd

Package boodd: bootstrap for dependent data

boot_dist

Bootstrap Distribution

boot_local

TFT Local Bootstrap.

boot_res

TFT Residual Bootstrap.

boot_wild

TFT wild bootstrap.

bootglm

Bootstrap for Generalized Linear Model

boots

Bootstrap for the I.I.D. Case

bootsemi

Semiparametric Bootstrap

bopt_circy

Optimal Bootstrap Block Length for Periodically Correlated Time Series...

class.boodd

Objects of Class boodd

compute_power

Compute the Power of a Statistical Test

confint.boodd

Calculate Confidence Intervals for boodd Objects.

embb-class

Characteristics for Extension of Moving Block Bootstrap Class

embb.sample

EMBB Method

f_PseudoBlocks

Compute the Value of the Function on a (Pseudo)-Regenerative Blocks.

fastNadaraya

Nadaraya-Watson Estimator for Transition Densities for Markov chains.

field.sub

Subsampling of Random Fields

fieldboot

Block Bootstrap of Random Field

fieldbootP

Bootstrap Sample from the Random Field

findBestEpsilon

Optimal Size of Small Sets

fourierEstimators

Fourier Coefficients Estimation of the Mean and Autocovariance Functio...

freqboot

Frequency Domain Bootstrap

ftrunc

Robust Estimators of the Mean Based on Regeneration Blocks.

func_fdb

Functional Bootstrap in the Frequency Domain (FDB)

genETARCH

Generate an Exponential TAR-ARCH Process

genMM1

Generate an M/M/1 Queue Process

GetBlocks

Compute Block Splitting for Atomic Markov Chains

GetPseudoBlocks

Computing Pseudo-regenerative Blocks

jackFunc

Jackknife Variance Function

jackFuncBlock

Jackknife Variance Function Using Blocks of Fixed Length

jackFuncRegen

Jackknife Variance Function for Markov Chains Using Regenerative Block...

jackVar

Jackknife Variance Estimator

jackVarBlock

Jackknife Variance Estimator Based on Fixed Length Blocks

jackVarField

Jackknife Variance for Random Fields Based on Blocks

jackVarRegen.atom

Jackknife Variance Estimator for Atomic Markov Chains

jackVarRegen

Jackknife Variance Estimator for Regenerative Processes

jackVarRegen.smallEnsemble

Jackknife Variance Estimation for General Harris Markov Chains

lam

Lag window

mark_boot

Bootstraping Markov chain

naradamar

Nadaraya-Watson Estimator for Transition Densities.

para.boot

Parametric Bootstrap for i.i.d. Data

per_boo

Bootstrap of Periodogram

pkc

Plot Kernel Density Estimates for Null and Alternative Distributions.

plot.boodd

Plot an Object of Class boodd

qVar

Estimating Variance of a Quantile

rate.block.sub

Block Subsampling for Time Series with Convergence Rate Estimation

rate.sub

Subsampling for i.i.d. Data with Convergence Rate Estimation

regenboot

Regenerative and Approximative Regenerative Block Bootstrap.

seasonalEstimators

Computes time domain characteristics of periodically correlated time s...

sieveboot

Autoregressive Sieve Bootstrap

smallEnsemble

Class smallEnsemble

summary.boodd

Summary for Objects of Class boodd

tboot_dist

Computation of the Bootstrap-t Distribution

tft_boot

TFT Bootstrap.

thetaARBB

Compute the Extremal Index for Non-Atomic Markov Chains Using Pseudo-R...

thetaRB

Compute the Extremal Index for Atomic Markov Chains Using Regenerative...

zi_inar_process

Generate a ZI-INAR Process

Companion package, functions, data sets, examples for the book Patrice Bertail and Anna Dudek (2025), Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted. Kreiss, J.-P. and Paparoditis, E. (2003) <doi:10.1214/aos/1074290332> Politis, D.N., and White, H. (2004) <doi:10.1081/ETC-120028836> Patton, A., Politis, D.N., and White, H. (2009) <doi:10.1080/07474930802459016> Tsybakov, A. B. (2018) <doi:10.1007/b13794> Bickel, P., and Sakov, A. (2008) <doi:10.1214/18-AOS1803> Götze, F. and Račkauskas, A. (2001) <doi:10.1214/lnms/1215090074> Politis, D. N., Romano, J. P., & Wolf, M. (1999, ISBN:978-0-387-98854-2) Carlstein E. (1986) <doi:10.1214/aos/1176350057> Künsch, H. (1989) <doi:10.1214/aos/1176347265> Liu, R. and Singh, K. (1992) <https://www.stat.purdue.edu/docs/research/tech-reports/1991/tr91-07.pdf> Politis, D.N. and Romano, J.P. (1994) <doi:10.1080/01621459.1994.10476870> Politis, D.N. and Romano, J.P. (1992) <https://www.stat.purdue.edu/docs/research/tech-reports/1991/tr91-07.pdf> Patrice Bertail, Anna E. Dudek. (2022) <doi:10.3150/23-BEJ1683> Dudek, A.E., Leśkow, J., Paparoditis, E. and Politis, D. (2014a) <https://ideas.repec.org/a/bla/jtsera/v35y2014i2p89-114.html> Beran, R. (1997) <doi:10.1023/A:1003114420352> B. Efron, and Tibshirani, R. (1993, ISBN:9780429246593) Bickel, P. J., Götze, F. and van Zwet, W. R. (1997) <doi:10.1007/978-1-4614-1314-1_17> A. C. Davison, D. Hinkley (1997) <doi:10.2307/1271471> Falk, M., & Reiss, R. D. (1989) <doi:10.1007/BF00354758> Lahiri, S. N. (2003) <doi:10.1007/978-1-4757-3803-2> Shimizu, K. .(2017) <doi:10.1007/978-3-8348-9778-7> Park, J.Y. (2003) <doi:10.1111/1468-0262.00471> Kirch, C. and Politis, D. N. (2011) <doi:10.48550/arXiv.1211.4732> Bertail, P. and Dudek, A.E. (2024) <doi:10.3150/23-BEJ1683> Dudek, A. E. (2015) <doi:10.1007/s00184-014-0505-9> Dudek, A. E. (2018) <doi:10.1080/10485252.2017.1404060> Bertail, P., Clémençon, S. (2006a) <https://ideas.repec.org/p/crs/wpaper/2004-47.html> Bertail, P. and Clémençon, S. (2006, ISBN:978-0-387-36062-1) Radulović, D. (2006) <doi:10.1007/BF02603005> Bertail, P. Politis, D. N. Rhomari, N. (2000) <doi:10.1080/02331880008802701> Nordman, D.J. Lahiri, S.N.(2004) <doi:10.1214/009053604000000779> Politis, D.N. Romano, J.P. (1993) <doi:10.1006/jmva.1993.1085> Hurvich, C. M. and Zeger, S. L. (1987, ISBN:978-1-4612-0099-4) Bertail, P. and Dudek, A. (2021) <doi:10.1214/20-EJS1787> Bertail, P., Clémençon, S. and Tressou, J. (2015) <doi:10.1111/jtsa.12105> Asmussen, S. (1987) <doi:10.1007/978-3-662-11657-9> Efron, B. (1979) <doi:10.1214/aos/1176344552> Gray, H., Schucany, W. and Watkins, T. (1972) <doi:10.2307/2335521> Quenouille, M.H. (1949) <doi:10.1111/j.2517-6161.1949.tb00023.x> Quenouille, M. H. (1956) <doi:10.2307/2332914> Prakasa Rao, B. L. S. and Kulperger, R. J. (1989) <https://www.jstor.org/stable/25050735> Rajarshi, M.B. (1990) <doi:10.1007/BF00050835> Dudek, A.E. Maiz, S. and Elbadaoui, M. (2014) <doi:10.1016/j.sigpro.2014.04.022> Beran R. (1986) <doi:10.1214/aos/1176349847> Maritz, J. S. and Jarrett, R. G. (1978) <doi:10.2307/2286545> Bertail, P., Politis, D., Romano, J. (1999) <doi:10.2307/2670177> Bertail, P. and Clémençon, S. (2006b) <doi:10.1007/0-387-36062-X_1> Radulović, D. (2004) <doi:10.1007/BF02603005> Hurd, H.L., Miamee, A.G. (2007) <doi:10.1002/9780470182833> Bühlmann, P. (1997) <doi:10.2307/3318584> Choi, E., Hall, P. (2000) <doi:10.1111/1467-9868.00244> Efron, B., Tibshirani, R. (1993, ISBN:9780429246593) Bertail, P., Clémençon, S. and Tressou, J. (2009) <doi:10.1007/s10687-009-0081-y> Bertail, P., Medina-Garay, A., De Lima-Medina, F. and Jales, I. (2024) <doi:10.1080/02331888.2024.2344670>.

  • Maintainer: Karolina Marek
  • License: GPL (>= 2)
  • Last published: 2025-06-05