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Thinking the Unthinkable: Modern Non-Parametric Re-sampling Methods

Giampiero Favato

Kingston University

Roger Mills

Henley Management College - Henley Centre for Value Improvement (HCVI)

August 2007

Classical parametric tests compare observed statistics to theoretical sampling distributions. Re-sampling is a revolutionary methodology because it departs from theoretical distributions; the inference is based upon repeated sampling within the same empirical sample. This definition encompasses Monte Carlo simulation, cross validation tests, jackknife and bootstrap procedures.
The need for such methods is heightened by the vast amounts of data being collected in the modern information age, and by the increasingly complex scientific questions being asked. These factors have together led to the rapid development of modern non-parametric methods. An appealing feature of re-sampling methods is the way they combine an intuitive and layman-friendly applied aspect with challenging and elegant mathematics.
Purpose of this review is to divulge the theoretical foundations of re-sampling and to facilitate a coherent use of this computational intensive research method.

Number of Pages in PDF File: 36

Keywords: resampling, statistical, method, non-parametric

JEL Classification: C14

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Date posted: September 13, 2007  

Suggested Citation

Favato, Giampiero and Mills, Roger, Thinking the Unthinkable: Modern Non-Parametric Re-sampling Methods (August 2007). Available at SSRN: http://ssrn.com/abstract=1012661 or http://dx.doi.org/10.2139/ssrn.1012661

Contact Information

Giampiero Favato (Contact Author)
Kingston University ( email )
Kingston Hill
Kingston upon Thames
Surrey KT2 7LB
United Kingdom
Roger Mills
Henley Management College - Henley Centre for Value Improvement (HCVI) ( email )
Oxfordshire RG9 3AU, England
United Kingdom
HOME PAGE: www.henleymc.ac.uk/hcvi
Feedback to SSRN

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