Journal of Agricultural Biological and Environmental Statistics最新文献

英文中文

Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models. 空间变化混合模型降水数据产品的分布验证。

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2023-01-01 DOI: 10.1007/s13253-022-00515-0

Lynsie R Warr, Matthew J Heaton, William F Christensen, Philip A White, Summer B Rupper

The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside of the polar regions. Because of the large population living in the Indus watershed region who are reliant on melt from these glaciers for fresh water, understanding the factors that affect glacial melt along with the impacts of climate change on the region is important for managing these natural resources. While there are multiple climate data products (e.g., reanalysis and global climate models) available to study the impact of climate change on this region, each product will have a different amount of skill in projecting a given climate variable, such as precipitation. In this research, we develop a spatially varying mixture model to compare the distribution of precipitation in the High Mountain Asia region as produced by climate models with the corresponding distribution from in situ observations from the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) data product. Parameter estimation is carried out via a computationally efficient Markov chain Monte Carlo algorithm. Each of the estimated climate distributions from each climate data product is then validated against APHRODITE using a spatially varying Kullback-Leibler divergence measure. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00515-0.

亚洲高山地区的冰川比地球上除极地地区以外的任何地方都要多。由于生活在印度河流域地区的大量人口依赖这些冰川融化获得淡水，因此了解影响冰川融化的因素以及气候变化对该地区的影响对于管理这些自然资源非常重要。虽然有多种气候数据产品(例如，再分析和全球气候模式)可用于研究气候变化对该地区的影响，但每种产品在预测给定气候变量(如降水)方面的技能程度不同。在这项研究中，我们建立了一个空间变化的混合模式，将气候模式产生的亚洲高山地区降水分布与亚洲降水-高分辨率观测数据整合评估(APHRODITE)数据产品现场观测的相应分布进行比较。参数估计是通过计算效率高的马尔可夫链蒙特卡罗算法进行的。然后，利用空间变化的Kullback-Leibler散度度量，根据APHRODITE对每个气候数据产品的每个估计气候分布进行验证。本文附带的补充资料出现在网上。本文的补充资料请参见10.1007/s13253-022-00515-0。

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引用次数: 1

Computational Efficiency and Precision for Replicated-Count and Batch-Marked Hidden Population Models. 复制计数和批量标记隐藏人口模型的计算效率和精度。

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2023-01-01 DOI: 10.1007/s13253-022-00509-y

Matthew R P Parker, Laura L E Cowen, Jiguo Cao, Lloyd T Elliott

We address two computational issues common to open-population N-mixture models, hidden integer-valued autoregressive models, and some hidden Markov models. The first issue is computation time, which can be dramatically improved through the use of a fast Fourier transform. The second issue is tractability of the model likelihood function for large numbers of hidden states, which can be solved by improving numerical stability of calculations. As an illustrative example, we detail the application of these methods to the open-population N-mixture models. We compare computational efficiency and precision between these methods and standard methods employed by state-of-the-art ecological software. We show faster computing times (a $\sim 6$ to $\sim 30$ times speed improvement for population size upper bounds of 500 and 1000, respectively) over state-of-the-art ecological software for N-mixture models. We also apply our methods to compute the size of a large elk population using an N-mixture model and show that while our methods converge, previous software cannot produce estimates due to numerical issues. These solutions can be applied to many ecological models to improve precision when logs of sums exist in the likelihood function and to improve computational efficiency when convolutions are present in the likelihood function. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00509-y.

我们解决了开放种群n混合模型、隐整值自回归模型和一些隐马尔可夫模型中常见的两个计算问题。第一个问题是计算时间，通过使用快速傅里叶变换可以显著改善计算时间。第二个问题是模型似然函数对于大量隐藏状态的可跟踪性，这可以通过提高计算的数值稳定性来解决。作为一个说明性的例子，我们详细介绍了这些方法在开放种群n-混合物模型中的应用。我们比较了这些方法和最先进的生态软件采用的标准方法之间的计算效率和精度。我们展示了比n -混合物模型的最先进的生态软件更快的计算时间(在种群规模上界分别为500和1000时，速度提高了~ 6到~ 30倍)。我们还应用我们的方法使用n混合模型来计算大型麋鹿种群的规模，并表明虽然我们的方法收敛，但由于数值问题，以前的软件无法产生估计。这些解决方案可以应用于许多生态模型，以提高在似然函数中存在和的对数时的精度，并提高在似然函数中存在卷积时的计算效率。本文附带的补充资料出现在网上。本文的补充资料请参见10.1007/s13253-022-00509-y。

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引用次数: 0

Correction to: Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality 修正:用连续易出错暴露估计因果暴露反应函数:细颗粒物和全因死亡率的研究

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2022-12-26 DOI: 10.1007/s13253-022-00526-x

K. Josey, P. deSouza, Xiao Wu, D. Braun, R. Nethery

引用次数: 0

Dynamic Population Models with Temporal Preferential Sampling to Infer Phenology 用时间优先抽样来推断物候的动态种群模型

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2022-12-10 DOI: 10.1007/s13253-023-00552-3

Michael R. Schwob, M. Hooten, Travis McDevitt-Galles

引用次数: 0

Correction to: Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series 修正:空间相关功能时间序列的异步变更点估计

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2022-12-02 DOI: 10.1007/s13253-022-00524-z

Mengchen Wang, Trevor Harris, Bo Li

引用次数: 0

Spatiotemporal Exposure Prediction with Penalized Regression 基于惩罚回归的时空暴露预测

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2022-11-17 DOI: 10.1007/s13253-022-00523-0

Nathan A. Ryder, Joshua P. Keller

引用次数: 0

Modeling Community Dynamics Through Environmental Effects, Species Interactions and Movement 通过环境影响、物种相互作用和运动建模群落动态

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2022-11-07 DOI: 10.1007/s13253-022-00520-3

Becky Tang, J. Clark, P. Marra, A. Gelfand

引用次数: 2

Review of Handbook of Graphs and Networks in People Analytics: With Examples in R and Python by Keith McNulty 回顾《人际分析中的图和网络手册:用R和Python举例》，作者:Keith McNulty

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2022-11-03 DOI: 10.1007/s13253-022-00521-2

Sa Ren

引用次数: 0

Spatiotemporal Event Studies for Environmental Data Under Cross-Sectional Dependence: An Application to Air Quality Assessment in Lombardy 横断面依赖下环境数据时空事件研究:在伦巴第地区空气质量评价中的应用

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2022-10-24 DOI: 10.1007/s13253-023-00564-z

Paolo Maranzano, M. Pelagatti

引用次数: 1

Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France 基于模型的法国河流硝酸盐浓度趋势和循环聚类

IF 1.4 4区数学 Q3 BIOLOGY

Journal of Agricultural Biological and Environmental Statistics

Pub Date : 2022-09-01 DOI: 10.1007/s13253-022-00513-2

M. Heiner, Matthew J. Heaton, Benjamin W. Abbott, P. White, C. Minaudo, R. Dupas

引用次数: 2

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