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An Alternative to Traditional Goodness-of-Fit Tests for Discretely Measured Continuous Data

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Traditional goodness-of-fit tests such as the Kolmogorov-Smirnov and 2 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.

Keywords: bootstrapping; crown density; empirical distribution function; percentiles

Document Type: Research Article

Publication date: October 1, 2007

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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