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A fixed count sampling estimator of stem density based on a survival function

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  • S. Magnussen

    (Natural Resources Canada, Canadian Forest Service, Victoria BC, Canada)

Abstract

In fixed count sampling (FCS) a fixed number (k) of observations is made at n randomly selected sample locations. For estimation of stem density, the distance from a random sample location to the k nearest trees was measured. It is known that practical FCS estimators of stem density are biased. With the objective of reducing bias in FCS estimators of stem density, a new estimator derived from a survival function with distance acting as time was presented. To allow for spatial heterogeneity in stem density, the survival function includes shared frailty. Encouraging results with k = 6 in terms of bias, root mean squared error (RMSE), and coverage of nominal 95% confidence intervals were obtained in an extensive testing with simulated random sampling from 54 actual and four simulated spatial point patterns of tree locations. Sample sizes were 9, 15, and 30, with 1200 replications per setting. The performance across sites of the new FCS estimator was variable but almost paralleled that of a design-based estimator with fixed area plots. Users of the new FCS estimator can expect an absolute relative bias and a root mean squared error that are 1% greater than for sampling with fixed area plots holding an average of k trees. The chance of a smaller RMSE with the proposed estimator was estimated at 0.44.

Suggested Citation

  • S. Magnussen, 2015. "A fixed count sampling estimator of stem density based on a survival function," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 61(11), pages 485-495.
    • Handle: RePEc:caa:jnljfs:v:61:y:2015:i:11:id:46-2015-jfs
    DOI: 10.17221/46/2015-JFS
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    References listed on IDEAS

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    1. Roberto G. Gutierrez, 2002. "Parametric frailty and shared frailty survival models," Stata Journal, StataCorp LP, vol. 2(1), pages 22-44, February.
    2. Yi Li & Louise Ryan, 2002. "Modeling Spatial Survival Data Using Semiparametric Frailty Models," Biometrics, The International Biometric Society, vol. 58(2), pages 287-297, June.
    3. Yi Li & Louise Ryan, 2004. "Survival Analysis With Heterogeneous Covariate Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 724-735, January.
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    Cited by:

    1. Hormoz SOHRABI, 2018. "Adaptive k-tree sample plot for the estimation of stem density: An empirical approach," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 64(1), pages 17-24.

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