Including model uncertainty in estimating variances in multiple capture studies |
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Authors: | James L Norris III Kenneth H Pollock |
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Institution: | (1) Department of Mathematics and Computer Science, Wake Forest University, 27109 Winston-Salem, NC, USA;(2) Department of Statistics, North Carolina State University, 27695 Raleigh, NC, USA |
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Abstract: | We present bootstrap-based methods which incorporate model uncertainty in estimating variances in multiple capture studies. Each of our three methods has a specific set of properties, and we discuss when each method should be used. Our first method can be used in any multiple capture setting, but it gives an estimate of the variance conditional on the number of observed animals. Our other two methods yield estimates of the unconditional variance; they require good estimates of part or all of the specific probability model, respectively. Smoothed estimated cell probabilities are utilized by the latter method. We contrast the three methods on a real-life data set, and then conduct simulations for a simple setting. Finally, we detail the use of our methodology for specific settings and discuss adaptations for tag-return studies. |
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Keywords: | Bootstrap full probability model model selection non-parametric statistics |
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