bootlib: Bootstrapping and Resampling with S-Plus. A library of functions and data written by Angelo Canty to accompany the Cambridge University Press publication Bootstrap Methods and Their Application by Anthony Davison and David Hinkley. Bootstrap methods are computer-intensive methods of statistical analysis using simulation to obtain reliable standard errors, confidence intervals, and other measures of uncertainty for a wide range of problems. This book gives a broad and up-to-date coverage of bootstrap methods with numerous applied examples, together with the basic theory without emphasis on mathematical vigour. The material of the book is covered in eleven chapters in addition to a bibliography and an appendix. Chapter 2 describes the properties of resampling methods for use with single samples from parametric and nonparametric models. Chapter 3 extends the basic ideas to several samples, semiparametric and smooth models. Significance and confidence intervals are the subjects of Chapters 4 and 5. The three subsequent chapters deal with resampling methods appropriate for linear regression models, generalized linear models and nonlinear models, and time series, spatial data and point processes. Chapter 9 describes how variance reduction techniques such as balanced simulation can be adapted to yield improved simulations. Chapter 10 describes various semiparametric versions of the likelihood function and the ideas underlying which are closely related to resampling methods. Chapter 11 gives a short introduction to the resampling routines in software packages

References in zbMATH (referenced in 426 articles , 1 standard article )

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  1. Combes, Catherine; Ng, Hon Keung Tony: On parameter estimation for amoroso family of distributions (2022)
  2. Najarzadeh, Dariush: Conservative confidence intervals on multiple correlation coefficient for high-dimensional elliptical data using random projection methodology (2022)
  3. Vilkkumaa, Eeva; Liesiö, Juuso: What causes post-decision disappointment? Estimating the contributions of systematic and selection biases (2022)
  4. Xu, Kai; Zhu, Liping: Power analysis of projection-pursuit independence tests (2022)
  5. Zhang, Feipeng; Yang, Jiejing; Liu, Lei; Yu, Yuan: Generalized linear-quadratic model with a change point due to a covariate threshold (2022)
  6. Asenova, Stefka; Mazo, Gildas; Segers, Johan: Inference on extremal dependence in the domain of attraction of a structured Hüsler-Reiss distribution motivated by a Markov tree with latent variables (2021)
  7. Bertarelli, G.; Chambers, R.; Salvati, N.: Outlier robust small domain estimation via bias correction and robust bootstrapping (2021)
  8. Borboudakis, Giorgos; Tsamardinos, Ioannis: Extending greedy feature selection algorithms to multiple solutions (2021)
  9. Braverman, Amy; Hobbs, Jonathan; Teixeira, Joaquim; Gunson, Michael: Post hoc uncertainty quantification for remote sensing observing systems (2021)
  10. Debayle, Johan; Ðogaš, Vesna Gotovac; Helisová, Kateřina; Staněk, Jakub; Zikmundová, Markéta: Assessing similarity of random sets via skeletons (2021)
  11. Economou, P.; Batsidis, A.; Tzavelas, G.; Bagkavos, D.: Hypothesis testing for the population mean and variance based on (r)-size biased samples (2021)
  12. Hees, Katharina; Nayak, Smarak; Straka, Peter: Statistical inference for inter-arrival times of extreme events in bursty time series (2021)
  13. Jammalamadaka, S. Rao; Guerrier, Stéphane; Mangalam, Vasudevan: A two-sample nonparametric test for circular data -- its exact distribution and performance (2021)
  14. Neumann, André; Bodnar, Taras; Dickhaus, Thorsten: Estimating the proportion of true null hypotheses under dependency: a marginal bootstrap approach (2021)
  15. Ng, Wai Leong; Yau, Chun Yip; Chen, Xinyuan: Frequency domain bootstrap methods for random fields (2021)
  16. Pfaffermayr, Michael: Confidence intervals for the trade cost parameters of cross-section gravity models (2021)
  17. Phadnis, Milind A.; Mayo, Matthew S.: Sample size calculations for noninferiority trials for time-to-event data using the concept of proportional time (2021)
  18. Polansky, Alan M.; Pramanik, Paramahansa: A motif building process for simulating random networks (2021)
  19. Ribeiro, Vinícius S. O.; Nobre, Juvêncio S.; dos Santos, José Roberto S.; Azevedo, Caio L. N.: Beta rectangular regression models to longitudinal data (2021)
  20. Rodriguez, Alejandro; Pino, Gabriel; Herrera, Rodrigo: A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models (2021)

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