Journal of Agricultural Biological and Environmental Statistics最新文献

Algorithms for Fitting the Space-Time ETAS Model to Earthquake Catalog Data: A Comparative Study 根据地震目录数据拟合时空 ETAS 模型的算法：比较研究

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-09-09 DOI: 10.1007/s13253-024-00650-w

Achmad Choiruddin, Annisa Auliya Rahman, Christopher Andreas

{"title":"Algorithms for Fitting the Space-Time ETAS Model to Earthquake Catalog Data: A Comparative Study","authors":"Achmad Choiruddin, Annisa Auliya Rahman, Christopher Andreas","doi":"10.1007/s13253-024-00650-w","DOIUrl":"https://doi.org/10.1007/s13253-024-00650-w","url":null,"abstract":"The space-time epidemic-type aftershock sequence (space-time ETAS) is a standard model for the analysis of earthquake catalogs. The model considers a semi-parametric conditional intensity function consisting of a semi-parametric background rate and a parametric aftershock rate. For the estimation procedure, the optimization employs an iterative algorithm where the nonparametric and parametric components are estimated iteratively using, respectively, kernel density estimation and maximum likelihood technique. ETAS and etasFLP are the two R packages that implement such a procedure with different techniques for estimating both the nonparametric and parametric components. The two packages have been studied from different directions and have not been evaluated together. This study examines the common features of the models and algorithms generated from the packages, and then evaluates their performance through simulation study and application to the Sumatran earthquake. For the analysis involving small or medium number of earthquakes, the etasFLP outperforms ETAS in terms of parameter estimation and computing time. For the application, we identify three main areas of high seismic risk: Simeulue Island, Nias Island, and southeast of Siberut Island.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175491","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

Bayesian Approaches to Proxy Uncertainty Quantification in Paleoecology: A Mathematical Justification and Practical Integration 古生态学中代理不确定性量化的贝叶斯方法：数学论证与实践整合

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-08-29 DOI: 10.1007/s13253-024-00647-5

Marco A. Aquino-López, Lysanna Anderson, Joan-Albert Sanchez-Cabeza, Ana Carolina Ruiz-Fernández, J. Andrés Christen

{"title":"Bayesian Approaches to Proxy Uncertainty Quantification in Paleoecology: A Mathematical Justification and Practical Integration","authors":"Marco A. Aquino-López, Lysanna Anderson, Joan-Albert Sanchez-Cabeza, Ana Carolina Ruiz-Fernández, J. Andrés Christen","doi":"10.1007/s13253-024-00647-5","DOIUrl":"https://doi.org/10.1007/s13253-024-00647-5","url":null,"abstract":"Paleoenvironmental data are essential for reconstructing environmental conditions in the distant past, and these reconstructions strongly depend on proxies and age–depth models. Proxies are indirect measurements that substitute for variables that cannot be directly measured, such as past precipitation. Conversely, an age–depth model is a tool that correlates the observed proxy with a specific moment in time. Bayesian age–depth modelling has proved to be a powerful method for estimating sediment ages and their associated uncertainties. However, there remains considerable potential for further integration into proxy analysis. In this paper, we explore a mathematical justification and a computational approach that integrates uncertainty at the age–depth level and propagates it to the proxy scale in the form of a posterior predictive distribution. This method mitigates potential biases and errors by removing the need to assign a single age to a given proxy measurement. It allows for quantifying the likelihood that proxy data values correspond to modelled ages, thus enabling the quantification of uncertainty in both the temporal and proxy value domains. The use of Bayesian statistics in proxy analysis represents a relatively recent advancement. We aim to mathematically justify incorporating the Markov chain Monte Carlo output from age–depth models into proxy analysis and to present a novel methodology for constructing environmental reconstructions using this approach.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175493","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

Stopping Rule Sampling to Monitor and Protect Endangered Species 停止规则采样以监测和保护濒危物种

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-08-29 DOI: 10.1007/s13253-024-00649-3

Lara Mitchell, Leo Polansky, Ken B. Newman

{"title":"Stopping Rule Sampling to Monitor and Protect Endangered Species","authors":"Lara Mitchell, Leo Polansky, Ken B. Newman","doi":"10.1007/s13253-024-00649-3","DOIUrl":"https://doi.org/10.1007/s13253-024-00649-3","url":null,"abstract":"Ecological science and management often require animal population abundance estimates to determine population status, set harvest limits on exploited populations, assess biodiversity, and evaluate the effects of management actions. However, sampling can harm animal populations. Motivated by trawl sampling of an endangered fish, we present a sequential adaptive sampling design focused on making population-level inferences while limiting harm to the target population. The design incorporates stopping rules such that multiple samples are collected at a site until one or more individuals from the target population are captured, conditional on the number of samples falling within a predetermined range. With this application in mind, we pair the stopping rules sampling design with a density model from which to base abundance indices. We use theoretical analyses and simulations to evaluate inference of population parameters and reduction in catch under the stopping rule sampling design compared to fixed sampling designs. Density point estimates based on stopping rules could theoretically be biased high, but simulations indicated that the stopping rules did not induce noticeable bias in practice. Retrospective analysis of the case study indicated that the stopping rules reduced catch by 60% compared to a fixed sampling design with maximum possible effort.Supplementary materials accompanying this paper appear online.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223220","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

Environmental Loss Assessment via Functional Outlier Detection of Transformed Biodiversity Profiles 通过变形生物多样性剖面的功能离群点检测进行环境损失评估

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-08-23 DOI: 10.1007/s13253-024-00648-4

Fabrizio Maturo, Annamaria Porreca

引用次数: 0

Expectations of Linear and Nonlinear Hawkes Processes Using a Field-Theoretical Approach 利用场论方法预期线性和非线性霍克斯过程

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2024-07-25 DOI: 10.1007/s13253-024-00644-8

Lirong Cui, Didier Sornette

{"title":"Expectations of Linear and Nonlinear Hawkes Processes Using a Field-Theoretical Approach","authors":"Lirong Cui, Didier Sornette","doi":"10.1007/s13253-024-00644-8","DOIUrl":"https://doi.org/10.1007/s13253-024-00644-8","url":null,"abstract":"Moments play a crucial role for understanding the mathematical properties and practical applications of Hawkes processes. Here, we derive expectations of Hawkes processes and their intensity functions using a recently introduced Markovian embedding of (generally non-Markovian) linear and nonlinear Hawkes processes via a field-theoretical approach. The necessary and sufficient conditions for the stability of the Hawkes processes are also given by using the expectations of intensity functions directly via some matrix manipulations. Two kinds of Hawkes processes are considered, the standard linear Hawkes process with non-Markovian memory function expressed as a finite sum of exponentials, and the nonlinear Hawkes process with an intensity function that is quadratic as a function of an internal variable (“tension”) itself expressed as the sum over all past events with memory function given as a finite sum of exponentials and with zero mean random marks. All results obtained for the quadratic Hawkes processes are new contributions to the literature. The results obtained for linear Hawkes processes recover already known conclusions, while providing a novel alternative approach to existing methods. The matrix method presented in this paper gives a new way for finding the necessary and sufficient conditions for the stability of Hawkes processes.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783498","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

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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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