Composite likelihood inference for space-time point processes.

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2025-01-07 DOI:10.1093/biomtc/ujaf009
Abdollah Jalilian, Francisco Cuevas-Pacheco, Ganggang Xu, Rasmus Waagepetersen
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Abstract

The dynamics of a rain forest is extremely complex involving births, deaths, and growth of trees with complex interactions between trees, animals, climate, and environment. We consider the patterns of recruits (new trees) and dead trees between rain forest censuses. For a current census, we specify regression models for the conditional intensity of recruits and the conditional probabilities of death given the current trees and spatial covariates. We estimate regression parameters using conditional composite likelihood functions that only involve the conditional first order properties of the data. When constructing assumption lean estimators of covariance matrices of parameter estimates, we only need mild assumptions of decaying conditional correlations in space, while assumptions regarding correlations over time are avoided by exploiting conditional centering of composite likelihood score functions. Time series of point patterns from rain forest censuses are quite short, while each point pattern covers a fairly big spatial region. To obtain asymptotic results, we therefore use a central limit theorem for the fixed timespan-increasing spatial domain asymptotic setting. This also allows us to handle the challenge of using stochastic covariates constructed from past point patterns. Conveniently, it suffices to impose weak dependence assumptions on the innovations of the space-time process. We investigate the proposed methodology by simulation studies and an application to rain forest data.

时空点过程的复合似然推理。
雨林的动态是极其复杂的,包括树木的出生、死亡和生长,以及树木、动物、气候和环境之间复杂的相互作用。我们在两次雨林普查之间考虑了新树(新树)和死树的模式。对于当前的人口普查,我们指定了给定当前树和空间协变量的新兵条件强度和死亡条件概率的回归模型。我们使用仅涉及数据的条件一阶属性的条件复合似然函数估计回归参数。在构造参数估计协方差矩阵的假设精益估计时,我们只需要对空间中衰减的条件相关进行温和的假设,而利用复合似然评分函数的条件定心避免了对时间相关的假设。热带雨林普查的点型时间序列很短,而每个点型覆盖了相当大的空间区域。为了得到渐近的结果,我们使用了一个中心极限定理,用于固定时间跨度增加的空间域渐近设置。这也允许我们处理使用从过去的点模式构建的随机协变量的挑战。方便的是,对时空过程的创新施加弱依赖假设就足够了。我们通过模拟研究和对雨林数据的应用来研究所提出的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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