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AI 根据所提供的论文材料归类:摘要聚焦林业生存分析与空间随机效应,未直接对应所列经济学细分方向。

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关注这篇论文的作者
Jie Li
Yili Hong
Ram Thapa
Harold E. Burkhart

含空间相关随机效应的火炬松生存分析

研究问题
研究树龄、间伐类型与地理区域对火炬松生存的关系时,如何调整不同观测地点之间的空间相关性,并估计生存模型?
方法与思路
作者采用半参数比例风险模型描述协变量关系,以空间随机效应表示地点间相关,并用 EM 算法估计,结合模拟和林业数据分析。
材料陈述的贡献
摘要提出结合空间随机效应的生存分析程序,通过模拟评估估计与方法比较,并应用于大规模火炬松生存数据。
阅读关注点
阅读时应核对空间随机效应的协方差设定、比例风险与删失处理,以及 EM 的计算实现、比较方法和协变量结果解释。
阅读边界
仅据版本未注明的 OpenAlex 索引摘要;空间结构、删失条件与具体实证发现未给出,不能判断稳健性或将关联视为因果结论及刊本结论。

原文摘要

Loblolly pine, a native pine species of the southeastern United States, is the most-planted species for commercial timber. Predicting survival of loblolly pine following planting is of great interest to researchers in forestry science as it is closely related to the yield of timber. Data were collected from a region-wide thinning study, where permanent plots, located at 182 sites ranging from central Texas east to Florida and north to Delaware, were established in 1980–1981. One of the main objectives of this study was to investigate the relationship between the survival of loblolly pine trees and several important covariates such as age, thinning types, and physiographic regions, while adjusting for spatial correlation among different sites. We use a semiparametric proportional hazards model to describe the effects of covariates on the survival time, and incorporate the spatial random effects in the model to describe the spatial correlation among different sites. We apply the expectation-maximization (EM) algorithm to estimate the parameters in the model and conduct simulations to validate the estimation procedure. We also compare the proposed method with existing methods through simulations and discussions. Then we apply the developed method to the large-scale loblolly pine tree survival data and interpret the results. We conclude this article with discussions on the advantages of the proposed method, major findings of data analysis, and directions for future research. Supplementary materials for this article are available online.

笔记与分组
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