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Titolo:
STOCHASTIC RESAMPLING TECHNIQUES FOR QUANTIFYING ERROR PROPAGATIONS IN FOREST FIELD EXPERIMENTS
Autore:
MAGNUSSEN S; BURGESS D;
Indirizzi:
FORESTRY CANADA,PACIFIC FORESTRY CTR,CANADIAN FOREST SERV,506 W BURNSIDE RD VICTORIA BC V8Z 1M5 CANADA
Titolo Testata:
Canadian journal of forest research
fascicolo: 5, volume: 27, anno: 1997,
pagine: 630 - 637
SICI:
0045-5067(1997)27:5<630:SRTFQE>2.0.ZU;2-Y
Fonte:
ISI
Lingua:
ENG
Soggetto:
MODELS; DENSITIES; EQUATION; VARIANCE; VOLUME;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
40
Recensione:
Indirizzi per estratti:
Citazione:
S. Magnussen e D. Burgess, "STOCHASTIC RESAMPLING TECHNIQUES FOR QUANTIFYING ERROR PROPAGATIONS IN FOREST FIELD EXPERIMENTS", Canadian journal of forest research, 27(5), 1997, pp. 630-637

Abstract

Statistical analyses of forest field experiments often do not addressuncertainties in model parameters and intermediate residuals and thusfail to account fully for the uncertainty inherent in the results. Stochastic resampling (bootstrap) provides a tool to integrate all knownsources of variation into the final results. We demonstrate stochastic resampling of data from a red pine (Pinus resinosa Ait.) spacing trial with two spacings and fear replications. Stochastic resampling resulted in higher among-plot variances of tree size and volume, which consequently lowered the significance level of pairwise t-tests of no spacing effect. The reliability of a direct analysis (no resampling) averaged 0.84. Stochastic resampling lowered the I-lest statistics by an average of 18% and their significance levels by about 75%. Resampling reversed the conclusion of 1 hypothesis out of 12 of no spacing effect,and changed the significance level of 2 tests from 0.06 to >0.09. Bootstrapped volume estimates were 1-6% higher than directly computed estimates because of nonlinear transformations of residuals in the volumeequations. Resampling techniques with a complete account of all relevant sources of variation hold promise for data analyses in forestry.

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Documento generato il 05/12/20 alle ore 02:05:11