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A Pòlya-Urn Resampling Scheme for Estimating Precision and Confidence Intervals Under One-Stage Cluster Sampling: Application to Map Classification Accuracy and Cover-Type Frequencies

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A Pòlya-urn resampling scheme (PURS) is introduced as an alternative to classical inference (CLAS) for applications related to map accuracy and estimating cover-type frequencies from land-cover maps. PURS is akin to a Bayesian bootstrap procedure for generating a predictive posterior distribution of sample estimates. PURS is a simple alternative to the complexity of deriving Taylor-series approximation (TSA) to CLAS variance estimators and reliance on asymptotic normality for construction of confidence intervals. The potential advantages of the resampling scheme are explored in simulated sampling of actual data from three 102–107-km2 large study sites. PURS root mean square errors (rmse) of six measures of classification accuracy were slightly lower than TSA to CLAS rmse. PURS estimated standard errors and coverage rates of confidence intervals (95%) for bias-corrected classified cover-type frequencies were, in most cases, superior to corresponding TSA. Yet variance estimators and confidence intervals constructed for a cover type occupying less than 5% of a study site were consistently underestimated by both methods. PURS is adaptable to a wide range of forest resource survey designs. FOR. SCI. 50(6):810–822.
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Keywords: Bayesian bootstrap; Classical inference; Taylor series approximation; confidence intervals; coverage rates; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources

Document Type: Regular Article

Affiliations: 1: Canadian Forest Service 506 West Burnside Rd. Victoria BC Canada V8Z 1M5, Fax: 1 (250) 363-0775, Email: [email protected] 2: SUNY ESF 320 Bray Hall Syracuse NY USA 13210, Email: [email protected] 3: Dipartimento di Scienze dell'Ambiente Forestale e delle Sue risorse Università degli Studi della Tuscia via San Camillo de Lellis s.n.c. Viterbo Italy 01100, Email: [email protected] 4: Canadian Forest Service 506 West Burnside Rd. Victoria BC Canada V8Z 1M5, Email: [email protected]

Publication date: 2004-12-01

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.

    2016 Impact Factor: 1.782 (Rank 17/64 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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