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Jackknife and Bootstrap Estimation for Sampling with Partial Replacement

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Abstract:

Jackknife and bootstrap estimators and variance estimators were compared with a classical estimator and variance estimator for sampling with partial replacement (SPR) on two occasions. One hundred twenty plots were sampled at time 1. At time 2, 10, 20, or 30 plots were remeasured, and a new sample size of size 20 was also selected. The samples were drawn from three large samples of forest plots from the northeastern United States, which were treated as populations. Although variables are correlated on the two occasions (r = 0.648 - 0.891), the assumptions of linearity and homogeneity of variance are questionable. The classical estimator is generally preferable to the jackknife and bootstrap estimators when both estimation bias and efficiency are important in SPR sampling. The jackknife variance estimator is generally preferable if variance estimation bias and confidence limit coverage rates are taken into consideration, particularly for skewed populations with small sample sizes. Generally, these jackknife variance estimates are less stable than the classical variance estimates. For. Sci. 33(3):676-689.

Keywords: Variance estimates; bias reduction; forest inventory; rotation sampling; successive sampling

Document Type: Journal Article

Affiliations: Research Forester, Forest Inventory and Analysis Project, Northeastern Forest Experiment Station, USDA Forest Service, Broomall, Pennsylvania

Publication date: 1987-09-01

More about this publication?
  • 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

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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