Resampling Stats

For more than a century the inherent difficulty of formula-based inferential statistics has baffled scientists, induced errors in research, and caused million of students to hate the subject.Complexity is the disease. Resampling (drawing repeated samples from the given data, or population suggested by the data) is a proven cure. Bootstrap, permutation, and other computer-intensive procedures have revolutionized statistics. Resampling is now the method of choice for confidence limits, hypothesis tests, and other everyday inferential problems.In place of the formidable formulas and mysterious tables of parametric and non-parametric tests based on complicated mathematics and arcane approximations, the basic resampling tools are simulations, created especially for the task at hand by practitioners who completely understand what they are doing and why they are doing it. Resampling lets you analyze most sorts of data, even those that cannot be analyzed with formulas.The growing stream of scientific articles using resampling techniques, both as a basic tool as well as for difficult applications, testifies to resampling’s value. And the swelling literature in mathematical statistics shows its acceptance on a theoretical basis, after many years in the wilderness. (Source:

References in zbMATH (referenced in 12 articles )

Showing results 1 to 12 of 12.
Sorted by year (citations)

  1. Kleiber, William; Katz, Richard W.; Rajagopalan, Balaji: Daily minimum and maximum temperature simulation over complex terrain (2013)
  2. Zhao, Zhibiao; Li, Xiaoye: Inference for modulated stationary processes (2013)
  3. Ricceri, F.; Fassino, C.; Matullo, G.; Roggero, M.; Torrente, M.-L.; Vineis, P.; Terracini, L.: Algebraic methods for studying interactions between epidemiological variables (2012)
  4. Kulik, Rafał; Wichelhaus, Cornelia: Nonparametric conditional variance and error density estimation in regression models with dependent errors and predictors (2011)
  5. Antoch, Jaromír: Environment for statistical computing (2008) ioport
  6. Figueredo, Grazziela P.; De Carvalho, Luis A. V.; Barbosa, Helio J. C.; Ebecken, Nelson F. F.: Evolutionary algorithms to simulate the phylogenesis of a binary artificial immune system (2008) ioport
  7. Maiwald, Thomas; Mammen, Enno; Nandi, Swagata; Timmer, Jens: Surrogate data -- a qualitative and quantitative analysis (2008)
  8. Abrahamson, Dor; Wilensky, Uri: Learning axes and bridging tools in a technology-based design for statistics (2007) MathEduc
  9. Bertail, Patrice; Clémençon, Stéphan: Second-order properties of regeneration-based bootstrap for atomic Markov chains (2007)
  10. Drton, Mathias; Perlman, Michael D.: Multiple testing and error control in Gaussian graphical model selection (2007)
  11. Bickel, Peter J.; Li, Bo: Regularization in statistics (2006)
  12. Mingoti, Sueli A.: A stepwise Bayesian estimator for the total number of distinct species in finite populations: sampling by elements (2000)