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Nonparametric Estimators for Variable Circular Plot Surveys

Pham Xuan Quang
Biometrics
Vol. 49, No. 3 (Sep., 1993), pp. 837-852
DOI: 10.2307/2532203
Stable URL: http://www.jstor.org/stable/2532203
Page Count: 16
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Nonparametric Estimators for Variable Circular Plot Surveys
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Abstract

A nonparametric estimator of animal density D is proposed for variable circular plot surveys, under the assumption that perfect radial detectability occurs at the station. The estimation of D requires estimating the derivative at zero of the probability density function of the detected radial distances; kernel techniques are used for that purpose. The estimator of D is shown to be normal for large and random samples, and a confidence interval for D is derived. A version of the Lindeberg Central Limit Theorem for random sample sizes is proved for the occasion. A bias reduction technique is proposed for the case of a radial detection function with a narrow shoulder. The technique is applied to data from several bird surveys for illustration. Small-sample properties are investigated by Monte Carlo simulations.

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