In a continuing effort to provide technical assistance to Forest Service personnel designing inventory and monitoring projects or attempting to analyze existing data sets, a web-based software application named Plot-GEM Plot Graphics of Estimation of the Mean to help address sampling issues was developed.
Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Plot-GEM can be used to answer many sampling questions, such as:. Plot-GEM can help answer these questions by providing visual displays graphs based on calculation of the percent error and thevconfidence interval for the estimate of the means of forest attributes. These calculations can be performed over a range of samplevsizes also referred to as sampling intensities.
Plot-GEM creates graphs so that the change in estimation accuracy, as sample sizevchanges, can be visualized. Plot-GEM also enables the user to explore their options and alternative accuracy objectives byvproviding graphs that depict the confidence intervals associated with different sample sizes.
The user can quickly explore "what if" scenarios for different numbers of plot, different suites of variables, and different ways of stratifying their sample.
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Plot-GEM uses a bootstrap algorithm to calculate the percent error and confidence interval for the estimates of the means. The bootstrap algorithm is well suited to the plot data situation because it makes no assumptions about the complicated correlation structure that exists among primary sampling units and subplots.
Rick Ullrich Assistant Director U. Mailstop: Washington, DC Plot-GEM can be used to answer many sampling questions, such as: Do we have enough plots to adequately address our issues? What sample size is necessary to achieve our accuracy goals?
How many more plots do we need? How accurate are our estimates based on the plots we have? How accurate is the current estimate of the mean?
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They are dramatically scarce and under-detailed. Nonetheless it is a worthwhile reading if you want to begin with bootstrap. Account Options Connexion. Version papier du livre. An Introduction to the Bootstrap.
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Bradley Efron , R. CRC Press , 15 mai - pages. Statistics is a subject of many uses and surprisingly few effective practitioners.
The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Random samples and probabilities. The empirical distribution function and the plugin.
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Standard errors and estimated standard errors. The bootstrap estimate of standard error. More complicated data structures. Regression models.
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Crossvalidation and other estimates of prediction. Adaptive estimation and calibration.
Assessing the error in bootstrap estimates. A geometrical representation for the bootstrap. An overview of nonparametric and parametric. Further topics in bootstrap confidence intervals.