Abstract
In this paper we present a weighted mixture distribution component counts (MDCC) approach for estimating total number of species. The proposed method combines conditional estimates of component counts from several candidate mixture distributions and uses bootstrap for confidence interval estimation. The distribution specification is flexible and can be adjusted to suit a variety of datasets. Smoothing techniques can also be incorporated to improve modeling of sparse data. The method is tested by a simulation study and applied to two microbiome datasets for illustration. Simulation results indicate improved bias, mean squared error and confidence interval coverage relative to comparison methods, as well as robustness to underlying data structure.
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