Journal of Agricultural Biological and Environmental Statistics最新文献_第2页

Physics-Guided Inverse Regression for Crop Quality Assessment 用于作物质量评估的物理引导反回归技术

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-07-17 DOI: 10.1007/s13253-024-00643-9

David Shulman, Assaf Israeli, Yael Botnaro, Ori Margalit, Oved Tamir, Shaul Naschitz, Dan Gamrasni, Ofer M. Shir, Itai Dattner

引用次数: 0

Efficient Augmented Block Designs for Unreplicated Test Treatments Along with Replicated Controls 无重复试验处理和重复对照的高效扩增区组设计

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-06-24 DOI: 10.1007/s13253-024-00629-7

Rahul Mukerjee

引用次数: 0

Random Graphical Model of Microbiome Interactions in Related Environments 相关环境中微生物群相互作用的随机图形模型

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-06-20 DOI: 10.1007/s13253-024-00638-6

Veronica Vinciotti, Ernst C. Wit, Francisco Richter

{"title":"Random Graphical Model of Microbiome Interactions in Related Environments","authors":"Veronica Vinciotti, Ernst C. Wit, Francisco Richter","doi":"10.1007/s13253-024-00638-6","DOIUrl":"https://doi.org/10.1007/s13253-024-00638-6","url":null,"abstract":"The microbiome constitutes a complex microbial ecology of interacting components that regulates important pathways in the host. Most microbial communities at various body sites tend to share common substructures of interactions, while also showing diversity related to the needs of the local environment. The aim of this paper is to develop a method for inferring both the common core and the differences in such microbiota systems. The approach combines two elements: (i) a random graph model generating networks across environments, and capturing potential relatedness at the structural level, with (ii) a Gaussian copula graphical model for the inference of environment-specific networks from multivariate microbial data. We propose a Bayesian approach for the joint inference of microbiota systems from metagenomic data for a number of body sites. The analysis of human microbiome data shows how the proposed random graphical model is able to capture varying levels of structural similarity across the different body sites and how this is supported by their taxonomical classification. Beyond a stable core, the inferred microbiome systems show interesting differences between the body sites, as well as interpretable relationships between various classes of microbes.\u0000","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"6 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Modified Bayesian Optimization Approach for Determining a Training Set to Identify the Best Genotypes from a Candidate Population in Genomic Selection 在基因组选育中确定从候选种群中识别最佳基因型的训练集的修正贝叶斯优化方法

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-06-19 DOI: 10.1007/s13253-024-00632-y

Hui-Ning Tu, Chen-Tuo Liao

{"title":"A Modified Bayesian Optimization Approach for Determining a Training Set to Identify the Best Genotypes from a Candidate Population in Genomic Selection","authors":"Hui-Ning Tu, Chen-Tuo Liao","doi":"10.1007/s13253-024-00632-y","DOIUrl":"https://doi.org/10.1007/s13253-024-00632-y","url":null,"abstract":"Training set optimization is a crucial factor affecting the probability of success for plant breeding programs using genomic selection. Conventionally, the training set optimization is developed to maximize Pearson’s correlation between true breeding values and genomic estimated breeding values for a testing population, because it is an essential component of genetic gain in plant breeding. However, many practical breeding programs aim to identify the best genotypes for target traits in a breeding population. A modified Bayesian optimization approach is therefore developed in this study to construct training sets for tackling such an interesting problem. The proposed approach is based on Monte Carlo simulation and data cross-validation, which is shown to be competitive with the existing methods developed to achieve the maximal Pearson’s correlation. Four real genome datasets, including two rice, one wheat, and one soybean, are analyzed in this study. An R package is generated to facilitate the application of the proposed approach. Supplementary materials accompanying this paper appear online.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"28 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Non-stationary Extensions of the Diffusion-Based Gaussian Matérn Field for Ecological Applications 生态应用中基于扩散的高斯马特恩场的非稳态扩展

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-05-31 DOI: 10.1007/s13253-024-00628-8

Juan Francisco Mandujano Reyes, Ian P. McGahan, Ting Fung Ma, Anne E. Ballmann, Daniel P. Walsh, Jun Zhu

{"title":"Non-stationary Extensions of the Diffusion-Based Gaussian Matérn Field for Ecological Applications","authors":"Juan Francisco Mandujano Reyes, Ian P. McGahan, Ting Fung Ma, Anne E. Ballmann, Daniel P. Walsh, Jun Zhu","doi":"10.1007/s13253-024-00628-8","DOIUrl":"https://doi.org/10.1007/s13253-024-00628-8","url":null,"abstract":"The use of statistical methods informed by partial differential equations (PDEs) and in particular reaction–diffusion PDEs such as ecological diffusion equations (EDEs) has been studied and used to model spatiotemporal processes. In this paper, we consider a stochastic extension of the EDE (SEDE) and discuss its interpretation and main differences from the deterministic EDE. We then leverage a non-stationary extension of the diffusion-based Gaussian Matérn field and show that this extension has SEDE-like behavior. The elucidated connection enables us to find a finite element approximated solution for SEDEs by means of the stochastic partial differential equation (SPDE) Bayesian method. For illustration, we analyze the evolution of white-nose syndrome (WNS) in the continental USA, comparing two models: stationary SEDE and a non-stationary pseudo-SEDE. Our results demonstrate the importance of non-stationarity in wildlife disease modeling and identify spatial explanatory variables for the non-stationarity in the WNS process. Finally, a simulation study is conducted to assess the deviance information criterion for differentiating from the two models, as well as the identifiability of the model parameters.Supplementary materials accompanying this paper appear online.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"5 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Inhomogeneous Weibull–Hawkes Process to Model Underdispersed Acoustic Cues 用非均质 Weibull-Hawkes 过程模拟分散不足的声学线索

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-05-11 DOI: 10.1007/s13253-024-00626-w

Alec B. M. Van Helsdingen, Tiago A. Marques, Charlotte M. Jones-Todd

引用次数: 0

A class of models for large zero-inflated spatial data 大型零膨胀空间数据的一类模型

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-04-29 DOI: 10.1007/s13253-024-00619-9

Ben Seiyon Lee, Murali Haran

{"title":"A class of models for large zero-inflated spatial data","authors":"Ben Seiyon Lee, Murali Haran","doi":"10.1007/s13253-024-00619-9","DOIUrl":"https://doi.org/10.1007/s13253-024-00619-9","url":null,"abstract":"Spatially correlated data with an excess of zeros, usually referred to as zero-inflated spatial data, arise in many disciplines. Examples include count data, for instance, abundance (or lack thereof) of animal species and disease counts, as well as semi-continuous data like observed precipitation. Spatial two-part models are a flexible class of models for such data. Fitting two-part models can be computationally expensive for large data due to high-dimensional dependent latent variables, costly matrix operations, and slow mixing Markov chains. We describe a flexible, computationally efficient approach for modeling large zero-inflated spatial data using the projection-based intrinsic conditional autoregression (PICAR) framework. We study our approach, which we call PICAR-Z, through extensive simulation studies and two environmental data sets. Our results suggest that PICAR-Z provides accurate predictions while remaining computationally efficient. An important goal of our work is to allow researchers who are not experts in computation to easily build computationally efficient extensions to zero-inflated spatial models; this also allows for a more thorough exploration of modeling choices in two-part models than was previously possible. We show that PICAR-Z is easy to implement and extend in popular probabilistic programming languages such as nimble and stan.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"18 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140808823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling First Arrival of Migratory Birds Using a Hierarchical Max-Infinitely Divisible Process 利用分层最大无限可分过程模拟候鸟的首次抵达

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-04-28 DOI: 10.1007/s13253-024-00624-y

Dhanushi A. Wijeyakulasuriya, Ephraim M. Hanks, Benjamin A. Shaby

引用次数: 0

Regularized Latent Trajectory Models for Spatio-temporal Population Dynamics 用于时空种群动力学的正则化潜在轨迹模型

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-04-01 DOI: 10.1007/s13253-024-00616-y

Xinyi Lu, Yoichiro Kanno, George P. Valentine, Matt A. Kulp, Mevin B. Hooten

{"title":"Regularized Latent Trajectory Models for Spatio-temporal Population Dynamics","authors":"Xinyi Lu, Yoichiro Kanno, George P. Valentine, Matt A. Kulp, Mevin B. Hooten","doi":"10.1007/s13253-024-00616-y","DOIUrl":"https://doi.org/10.1007/s13253-024-00616-y","url":null,"abstract":"Climate change impacts ecosystems variably in space and time. Landscape features may confer resistance against environmental stressors, whose intensity and frequency also depend on local weather patterns. Characterizing spatio-temporal variation in population responses to these stressors improves our understanding of what constitutes climate change refugia. We developed a Bayesian hierarchical framework that allowed us to differentiate population responses to seasonal weather patterns depending on their “sensitive” or “resilient” states. The framework inferred these sensitivity states based on latent trajectories delineating dynamic state probabilities. The latent trajectories are composed of linear initial conditions, functional regression models, and additive random effects representing ecological mechanisms such as topological buffering and effects of legacy weather conditions. Further, we developed a Bayesian regularization strategy that promoted temporal coherence in the inferred states. We demonstrated our hierarchical framework and regularization strategy using simulated examples and a case study of native brook trout (Salvelinus fontinalis) count data from the Great Smoky Mountains National Park, southeastern USA. Our study provided insights into ecological processes influencing brook trout sensitivity. Our framework can also be applied to other species and ecosystems to facilitate management and conservation.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"12 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska 使用稀疏采样激光雷达支持森林资源清查和小面积估算的模型：阿拉斯加塔纳纳地区 G-LiHT 激光雷达案例研究

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-03-13 DOI: 10.1007/s13253-024-00611-3

Andrew O. Finley, Hans-Erik Andersen, Chad Babcock, Bruce D. Cook, Douglas C. Morton, Sudipto Banerjee

{"title":"Models to Support Forest Inventory and Small Area Estimation Using Sparsely Sampled LiDAR: A Case Study Involving G-LiHT LiDAR in Tanana, Alaska","authors":"Andrew O. Finley, Hans-Erik Andersen, Chad Babcock, Bruce D. Cook, Douglas C. Morton, Sudipto Banerjee","doi":"10.1007/s13253-024-00611-3","DOIUrl":"https://doi.org/10.1007/s13253-024-00611-3","url":null,"abstract":"A two-stage hierarchical Bayesian model is developed and implemented to estimate forest biomass density and total given sparsely sampled LiDAR and georeferenced forest inventory plot measurements. The model is motivated by the United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) objective to provide biomass estimates for the remote Tanana Inventory Unit (TIU) in interior Alaska. The proposed model yields stratum-level biomass estimates for arbitrarily sized areas. Model-based estimates are compared with the TIU FIA design-based post-stratified estimates. Model-based small area estimates (SAEs) for two experimental forests within the TIU are compared with each forest’s design-based estimates generated using a dense network of independent inventory plots. Model parameter estimates and biomass predictions are informed using FIA plot measurements, LiDAR data that are spatially aligned with a subset of the FIA plots, and complete coverage remotely detected data used to define landuse/landcover stratum and percent forest canopy cover. Results support a model-based approach to estimating forest parameters when inventory data are sparse or resources limit collection of enough data to achieve desired accuracy and precision using design-based methods. Supplementary materials accompanying this paper appear on-line","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"145 17 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0