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Journal of Agricultural Biological and Environmental Statistics最新文献

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A Variance Partitioning Multi-level Model for Forest Inventory Data with a Fixed Plot Design 固定样地设计下森林清查数据的方差划分多级模型
4区 数学 Q3 BIOLOGY Pub Date : 2023-05-30 DOI: 10.1007/s13253-023-00548-z
Isa Marques, Paul F. V. Wiemann, Thomas Kneib
Abstract Forest inventories are often carried out with a particular design, consisting of a multi-level structure of observation plots spread over a larger domain and a fixed plot design of exact observation locations within these plots. Consequently, the resulting data are collected intensively within plots of equal size but with much less intensity at larger spatial scales. The resulting data are likely to be spatially correlated both within and between plots, with spatial effects extending over two different areas. However, a Gaussian process model with a standard covariance structure is generally unable to capture dependence at both fine and coarse scales of variation as well as for their interaction. In this paper, we develop a computationally feasible multi-level spatial model that accounts for dependence at multiple scales. We use a data-driven approach to determine the weight of each spatial process in the model to partition the variability of the measurements. We use simulated and German small tree inventory data to evaluate the model’s performance.Supplementary material to this paper is provided online.
森林资源调查通常有特定的设计,包括分布在更大范围内的多层次观测地块结构和这些地块内精确观测位置的固定地块设计。因此,所得到的数据集中收集在相同大小的地块内,但在较大的空间尺度上强度要小得多。得到的数据很可能在地块内部和地块之间具有空间相关性,空间效应延伸到两个不同的区域。然而,具有标准协方差结构的高斯过程模型通常无法捕获细尺度和粗尺度变化的依赖性以及它们的相互作用。在本文中,我们开发了一个计算上可行的多层次空间模型,该模型考虑了多尺度上的依赖性。我们使用数据驱动的方法来确定模型中每个空间过程的权重,以划分测量的可变性。我们使用模拟和德国的小树库存数据来评估模型的性能。本文的补充材料在网上提供。
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引用次数: 0
Review of “Mixed-Effects Models and Small Area Estimation” by Shonosuke Sugasawa and Tatsuya Kubokawa Sugasawa Shonosuke和Kubokawa Tatsuya的“混合效应模型和小面积估算”综述
IF 1.4 4区 数学 Q3 BIOLOGY Pub Date : 2023-05-16 DOI: 10.1007/s13253-023-00536-3
Desak Made Goldyna Rarasari
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引用次数: 0
Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” 关于“气候集合中储存的节约:基于模型的随机方法”的讨论
IF 1.4 4区 数学 Q3 BIOLOGY Pub Date : 2023-05-11 DOI: 10.1007/s13253-023-00540-7
J. Bessac, Robert Underwood, S. Di
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引用次数: 0
Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” 关于“气候集合中储存的节约:基于模型的随机方法”的讨论
IF 1.4 4区 数学 Q3 BIOLOGY Pub Date : 2023-05-11 DOI: 10.1007/s13253-023-00537-2
A. Poppick
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引用次数: 0
Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach 气候集合中的储存节约:基于模型的随机方法
IF 1.4 4区 数学 Q3 BIOLOGY Pub Date : 2023-05-11 DOI: 10.1007/s13253-022-00518-x
Huang Huang, S. Castruccio, A. Baker, M. Genton
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引用次数: 3
Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” 关于“气候集合中储存的节约:基于模型的随机方法”的讨论
IF 1.4 4区 数学 Q3 BIOLOGY Pub Date : 2023-05-11 DOI: 10.1007/s13253-023-00539-0
A. Datta
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引用次数: 0
Response to: Saving Storage in Climate Ensembles 响应:在气候集合中节约储存
IF 1.4 4区 数学 Q3 BIOLOGY Pub Date : 2023-05-11 DOI: 10.1007/s13253-023-00538-1
Wesley S. Burr
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引用次数: 0
Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” by Huang Huang, Stefano Castruccio, Allison H. Baker and Marc Genton 黄黄、Stefano Castruccio、Allison H. Baker和Marc Genton对“在气候集合中节约储存:基于模型的随机方法”的讨论
IF 1.4 4区 数学 Q3 BIOLOGY Pub Date : 2023-05-11 DOI: 10.1007/s13253-023-00541-6
S. Banerjee
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引用次数: 0
Rejoinder on ‘Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach’ 对“在气候集合中节约储存:一种基于模型的随机方法”的反驳
IF 1.4 4区 数学 Q3 BIOLOGY Pub Date : 2023-05-11 DOI: 10.1007/s13253-023-00542-5
Huang Huang, S. Castruccio, A. Baker, M. Genton
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引用次数: 0
Water Resource Surveillance for the Salton Sea in California By Adaptive Sequential Monitoring of Its Landsat Images 基于陆地卫星图像自适应序列监测的加州索尔顿海水资源监测
IF 1.4 4区 数学 Q3 BIOLOGY Pub Date : 2023-05-05 DOI: 10.1007/s13253-023-00545-2
Fan Yi, P. Qiu
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引用次数: 0
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Journal of Agricultural Biological and Environmental Statistics
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