2017/05/21 by François Koladjo, Koladjo, François, Mesrob I. Ohannessian +3
Computer Science · Mathematics · Medicine · #Bayesian Methods and Mixture Models #Census and Population Estimation #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1705.07509
openalex publication_date 2017/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a truncation model for abundance distribution in the species richness estimation. This model is inherently semiparametric and incorporates an unknown truncation threshold between rare and abundant counts observations. Using the conditional likelihood, we derive a class of estimators for the parameters in the model by a stepwise maximisation. The species richness estimator is given by the integer maximising the binomial likelihood when all other parameters in the model are know. Under regularity conditions, we show that the estimators of the model parameters are asymptotically efficient. We recover the Chao's lower bound estimator of species richeness when the model is a unicomponent Poisson's model. So, it is an element of our class of estimators. In a simulation study, we show the performances of the proposed method and compare it to some others.