Bayesian Estimation of Animal Abundance for Line Transect Sampling
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Summary: This paper develops a Bayesian model for estimating the density of a closed animal population from data obtained by the line transect method. The detection function is assumed to be half-normal and the data are neither grouped nor truncated. A Bayes estimator is constructed with respect to a gamma prior density and two loss functions: relative squared error and absolute squared error. The Bayes estimator is compared with the maximum likelihood estimator as well as the Fourier series estimator using two data sets.
line transect sampling, Bayesian inference, maximum likelihood estimator, gamma prior density, Applications of statistics to biology and medical sciences; meta analysis, closed animal population, Bayes estimator, density estimation, half-normal density, relative squared error, Fourier series estimator, absolute squared error
line transect sampling, Bayesian inference, maximum likelihood estimator, gamma prior density, Applications of statistics to biology and medical sciences; meta analysis, closed animal population, Bayes estimator, density estimation, half-normal density, relative squared error, Fourier series estimator, absolute squared error
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