Approximate tolerance limits for Zipf–Mandelbrot distributions
Zipf–Mandelbrot distributions are commonly used to model natural phenomena where the frequency of an event’s occurrence is inversely proportional to its rank based on that frequency of occurrence. This discrete distribution typically exhibits a large number of rare events; however, it may be of interest to obtain reasonable limits that bound the majority of the number of different events. We propose the use of statistical tolerance limits as a way to quantify such a bound. The tolerance limits are constructed using Wald confidence limits for the Zipf–Mandelbrot parameters and are shown through a simulation study to have coverage probabilities near the nominal levels. We also calculate Zipf–Mandelbrot tolerance limits for two real datasets and discuss the associated computer code developed for the R programming language.
Volume (Year): 392 (2013)
Issue (Month): 7 ()
|Contact details of provider:|| Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Sinha, Sitabhra, 2006.
"Evidence for power-law tail of the wealth distribution in India,"
Physica A: Statistical Mechanics and its Applications,
Elsevier, vol. 359(C), pages 555-562.
- Sitabhra Sinha, 2005. "Evidence for Power-law tail of the Wealth Distribution in India," Papers cond-mat/0502166, arXiv.org.
- Montemurro, Marcelo A., 2001. "Beyond the Zipf–Mandelbrot law in quantitative linguistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 300(3), pages 567-578.
- Zornig, Peter & Altmann, Gabriel, 1995. "Unified representation of Zipf distributions," Computational Statistics & Data Analysis, Elsevier, vol. 19(4), pages 461-473, April.
- Young, Derek S., 2010. "tolerance: An R Package for Estimating Tolerance Intervals," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i05).
- Kii, Masanobu & Akimoto, Keigo & Doi, Kenji, 2012. "Random-growth urban model with geographical fitness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(23), pages 5960-5970. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:392:y:2013:i:7:p:1702-1711. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If references are entirely missing, you can add them using this form.