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arXiv · 2603.08276

A Unified Framework for Density Estimation under Right-Censored Point-Centred Quarter Sampling

Wenzhe Huang·Guochun Shen·Dingliang Xing·Jiangyan Zhao
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Abstract

While the point-centred quarter method (PCQM) is widely used for density estimation, existing methods for handling right-censored data from truncated search radii rely primarily on a Poisson model assuming complete spatial randomness (CSR), leaving a critical gap for spatially aggregated populations. To address this limitation, we develop a unified likelihood- and moment-based framework for right-censored point-centred quarter sampling under both Poisson and negative binomial distribution (NBD) models. In particular, the proposed NBD-based estimators explicitly account for spatial aggregation and censoring simultaneously, extending distance-based inference beyond the CSR setting. Extensive simulations and applications to fully mapped forest plots reveal that the NBD-based MLE delivers the most robust overall performance across diverse ecological scenarios. Across more than 100 species from fully mapped forest plots, the proposed NBD-based MLE approximately reduced absolute relative bias by a median of 0.10 compared with existing censored estimators, representing a relative improvement of over 30%. Ultimately, our framework provides a rigorously validated and practically useful toolkit for analysing censored point-to-tree distance data.

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Wenzhe Huang, Guochun Shen, Dingliang Xing, Jiangyan Zhao. 2026-05-27. A Unified Framework for Density Estimation under Right-Censored Point-Centred Quarter Sampling. https://arxiv.org/abs/2603.08276

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