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Nonparametric methods

The nonparametric methods is a class of methods that do not require the data set under analysis to meet certain assumptions or parameters. Non-parametric methods will often be used when population data have an unknown distribution or when the sample size is small. Non-parametric statistics include both descriptive statistics and statistical inference.

 

Useful literature

  1. Hollander, Myles, et al. Nonparametric Statistical Methods, John Wiley & Sons, Incorporated, 2013 (access via the HSE Library).
  2. Corder, Gregory W., and Dale I. Foreman. Nonparametric Statistics : A Step-By-Step Approach, John Wiley & Sons, Incorporated, 2014 (access via the HSE Library).
  3. Govindarajulu, Zakkula, and Zakkula Govindarajulu. Nonparametric Inference, World Scientific Publishing Co Pte Ltd, 2007 (access via the HSE Library).
  4. Hettmansperger, Thomas P., and Joseph W. McKean. Robust Nonparametric Statistical Methods, Chapman and Hall/CRC, 2010 (access via the HSE Library).
  5. Bayesian Nonparametrics, edited by Nils Lid Hjort, et al., Cambridge University Press, 2010 (access via the HSE Library).

 

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