A New Method for Estimating Population Size from Removal Data 论文

摘要

Summary The theory leading to the maximum likelihood (ML) estimation of population size from removal data is reviewed. The assumptions of the removal method are that ehanges in population size occur only through eapture, and the probability of eapture is equal for all individuals in a population during the removal sequenee. A modifieation of the multinomial model is proposed and a new estimator developed. In the new model the likelihood density of the probability of capture is weighted with a beta prior. The ease where oe = d = 1 (uniform prior) is eompared with ML estimation andfornd to have lower bias and varianee. The new method, unlike previous methods, does not fail for aS?y eateh veetor thus avoiding the substitution of the total eateh for the estimate of N when infinite7 estimates occur. The assumptions that result from applying large sample theory while estimQting the varianee of ML estimates are reviewed, and a eondition presentedfor the inadequacy of avymptotie varianee formulae when using the weighted estimator (oe = F = 1). Examples illustrating the use of the new method are given, one example illustrates the use of the new method when previous methods fail. Various assumption violations are investigated and the new method is found to be more robust against the violation of assumptions than previous methods.