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

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

Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study 探索大型空间数据集的统计和深度学习方法的有效性：案例研究

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-02-08 DOI: 10.1007/s13253-024-00602-4

Arnab Hazra, Pratik Nag, Rishikesh Yadav, Ying Sun

{"title":"Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study","authors":"Arnab Hazra, Pratik Nag, Rishikesh Yadav, Ying Sun","doi":"10.1007/s13253-024-00602-4","DOIUrl":"https://doi.org/10.1007/s13253-024-00602-4","url":null,"abstract":"Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is motivated by the KAUST Competition on Large Spatial Datasets 2023, which tasked participants with estimating spatial covariance-related parameters and predicting values at testing sites, along with uncertainty estimates. We compared various statistical and deep learning approaches through cross-validation and ultimately selected the Vecchia approximation technique for model fitting. To overcome the constraints in the R package GpGp, which lacked support for fitting zero-mean Gaussian processes and direct uncertainty estimation—two things that are necessary for the competition, we developed additional R functions. Besides, we implemented certain subsampling-based approximations and parametric smoothing for skewed sampling distributions of the estimators. Our team DesiBoys secured the first position in two out of four sub-competitions and the second position in the other two, validating the effectiveness of our proposed strategies. Moreover, we extended our evaluation to a large real spatial satellite-derived dataset on total precipitable water, where we compared the predictive performances of different models using multiple diagnostics.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"527 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759689","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

Two Tests of Significance for Preferred Direction in Tree Radial Growth Under a Linear-Circular Regression Model with Correlated Random Errors 在具有相关随机误差的线性-圆形回归模型下，对树木径向生长首选方向的两个显著性检验

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-01-31 DOI: 10.1007/s13253-023-00599-2

Pierre Dutilleul, Tomoaki Imoto, Kunio Shimizu

{"title":"Two Tests of Significance for Preferred Direction in Tree Radial Growth Under a Linear-Circular Regression Model with Correlated Random Errors","authors":"Pierre Dutilleul, Tomoaki Imoto, Kunio Shimizu","doi":"10.1007/s13253-023-00599-2","DOIUrl":"https://doi.org/10.1007/s13253-023-00599-2","url":null,"abstract":"To analyze tree growth statistically through annual ring widths measured in 2-D horizontal trunk sections, we propose two tests of significance defined under a linear-circular regression model with fixed trigonometric effects and normal random errors with a variance-covariance structure from the symmetric circulant family. The associated von Mises distribution has a preferred direction parameter. Accordingly, the first test aims to assess the presence of a preferred direction in the radial growth of a tree from the center of its trunk in a given year. Assuming there is a preferred direction of radial growth for the tree in two years, the second test extends the first one by assessing the equality of tree radial growth in the two preferred directions. Both tests of significance are modified F-tests with the denominator df adjusted for the presence of autocorrelation. Their validity is analyzed for two autoregressive symmetric circulant correlation structures, as a function of the number (n) of angular data and the autocorrelation parameter value. Effects of the inter-year correlation coefficient value are also studied in the two-year case. The performance of REstricted Maximum Likelihood as estimation method is scrutinized in an extensive Monte Carlo study, and the power of the tests is analyzed when valid. The new testing procedures are applied with (n = 32, 64) ring widths per year for a white spruce tree during 18 years of growth until its harvest. R codes are available. Conclusions and perspectives for future research are given. Supplementary materials accompanying this paper appear on-line.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"231 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656375","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