Skip to Main Content
An alternative to traditional goodness-of-fit tests for discretely measured continuous dataAuthor(s): KaDonna C. Randolph; Bill Seaver
Source: Forest Science, Vol. 53(5): 590-599
Publication Series: Miscellaneous Publication
PDF: View PDF (233 KB)
DescriptionTraditional goodness-of-fit tests such as the Kolmogorov-Smirnov and x2 tests are easily applied to data of the continuous or discrete type, respectively. Occasionally, however, the case arises when continuous data are recorded into discrete categories due to an imprecise measurement system. In this instance, the traditional goodness-of-fit tests may not be wholly applicable because of an unmanageable number of ties in the data, sparse contingency tables, or both; therefore, a flexible alternative to goodness-of-fit tests for discretely measured continuous data is presented. The proposed methodology bootstraps confidence intervals for the difference between selected percentiles of the empirical distribution functions of two samples. Application of the approach is illustrated with a comparison of loblolly pine (Pinus taeda L.) tree crown density distributions at the 10th, 25th, 50th, 75th, and 90th percentiles simultaneously.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationRandolph, KaDonna C.; Seaver, Bill 2007. An alternative to traditional goodness-of-fit tests for discretely measured continuous data. Forest Science, Vol. 53(5): 590-599
Keywordsbootstrapping, crown density, empirical distribution function, percentiles
- New methods for estimating parameters of weibull functions to characterize future diameter distributions in forest stands
- Use of the Weibull function to predict future diameter distributions from current plot data
- A comparison of pine height models for the Crossett Experimental Forest
XML: View XML