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
- Hollander, Myles, et al. Nonparametric Statistical Methods, John Wiley & Sons, Incorporated, 2013 (access via the HSE Library).
- Corder, Gregory W., and Dale I. Foreman. Nonparametric Statistics : A Step-By-Step Approach, John Wiley & Sons, Incorporated, 2014 (access via the HSE Library).
- Govindarajulu, Zakkula, and Zakkula Govindarajulu. Nonparametric Inference, World Scientific Publishing Co Pte Ltd, 2007 (access via the HSE Library).
- Hettmansperger, Thomas P., and Joseph W. McKean. Robust Nonparametric Statistical Methods, Chapman and Hall/CRC, 2010 (access via the HSE Library).
- Bayesian Nonparametrics, edited by Nils Lid Hjort, et al., Cambridge University Press, 2010 (access via the HSE Library).
Have you spotted a typo?
Highlight it, click Ctrl+Enter and send us a message. Thank you for your help!
To be used only for spelling or punctuation mistakes.