Pub Date : 2023-11-09DOI: 10.1007/s13253-023-00580-z
Paul F. V. Wiemann, Matthias Katzfuss
{"title":"Bayesian Nonparametric Generative Modeling of Large Multivariate Non-Gaussian Spatial Fields","authors":"Paul F. V. Wiemann, Matthias Katzfuss","doi":"10.1007/s13253-023-00580-z","DOIUrl":"https://doi.org/10.1007/s13253-023-00580-z","url":null,"abstract":"","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241901","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}
Pub Date : 2023-11-06DOI: 10.1007/s13253-023-00576-9
Bappa Saha, Ankur Biswas, Tauqueer Ahmad, Nobin Chandra Paul
{"title":"Geographically Weighted Regression-Based Model Calibration Estimation of Finite Population Total Under Geo-referenced Complex Surveys","authors":"Bappa Saha, Ankur Biswas, Tauqueer Ahmad, Nobin Chandra Paul","doi":"10.1007/s13253-023-00576-9","DOIUrl":"https://doi.org/10.1007/s13253-023-00576-9","url":null,"abstract":"","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679106","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}
Pub Date : 2023-11-02DOI: 10.1007/s13253-023-00579-6
Jing Zhang, Ainsley Helling, A. John Bailer
{"title":"Comparing Methods for Determining Power Priors Based on Different Congruence Measures","authors":"Jing Zhang, Ainsley Helling, A. John Bailer","doi":"10.1007/s13253-023-00579-6","DOIUrl":"https://doi.org/10.1007/s13253-023-00579-6","url":null,"abstract":"","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135973180","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}
Pub Date : 2023-10-27DOI: 10.1007/s13253-023-00577-8
Daniela Silva, Raquel Menezes, Ana Moreno, Ana Teles-Machado, Susana Garrido
Abstract Scientific tools capable of identifying distribution patterns of species are important as they contribute to improve knowledge about biodiversity and species dynamics. The present study aims to estimate the spatiotemporal distribution of sardine ( Sardina pilchardus , Walbaum 1792) in the Portuguese continental waters, relating the spatiotemporal variability of biomass index with the environmental conditions. Acoustic data was collected during Portuguese spring acoustic surveys (PELAGO) over a total of 16,370 hauls from 2000 to 2020 (gap in 2012). We propose a spatiotemporal species distribution model that relies on a two-part model for species presence and biomass under presence, such that the biomass process is defined as the product of these two processes. Environmental information is incorporated with time lags, allowing a set of lags with associated weights to be suggested for each explanatory variable. This approach makes the model more complete and realistic, capable of reducing prediction bias and mitigating outliers in covariates caused by extreme events. In addition, based on the posterior predictive distributions obtained, we propose a method of classifying the occupancy areas by the target species within the study region. This classification provides a quite helpful tool for decision makers aiming at marine sustainability and conservation. Supplementary materials accompanying this paper appear on-line.
{"title":"Environmental Effects on the Spatiotemporal Variability of Sardine Distribution Along the Portuguese Continental Coast","authors":"Daniela Silva, Raquel Menezes, Ana Moreno, Ana Teles-Machado, Susana Garrido","doi":"10.1007/s13253-023-00577-8","DOIUrl":"https://doi.org/10.1007/s13253-023-00577-8","url":null,"abstract":"Abstract Scientific tools capable of identifying distribution patterns of species are important as they contribute to improve knowledge about biodiversity and species dynamics. The present study aims to estimate the spatiotemporal distribution of sardine ( Sardina pilchardus , Walbaum 1792) in the Portuguese continental waters, relating the spatiotemporal variability of biomass index with the environmental conditions. Acoustic data was collected during Portuguese spring acoustic surveys (PELAGO) over a total of 16,370 hauls from 2000 to 2020 (gap in 2012). We propose a spatiotemporal species distribution model that relies on a two-part model for species presence and biomass under presence, such that the biomass process is defined as the product of these two processes. Environmental information is incorporated with time lags, allowing a set of lags with associated weights to be suggested for each explanatory variable. This approach makes the model more complete and realistic, capable of reducing prediction bias and mitigating outliers in covariates caused by extreme events. In addition, based on the posterior predictive distributions obtained, we propose a method of classifying the occupancy areas by the target species within the study region. This classification provides a quite helpful tool for decision makers aiming at marine sustainability and conservation. Supplementary materials accompanying this paper appear on-line.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235021","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}
Pub Date : 2023-10-16DOI: 10.1007/s13253-023-00570-1
Alexandre Bohyn, Eric D. Schoen, Chee Ping Ng, Kristina Bishard, Manon Haarmans, Sebastian J. Trietsch, Peter Goos
{"title":"Design and Analysis of a Microplate Assay in the Presence of Multiple Restrictions on the Randomization","authors":"Alexandre Bohyn, Eric D. Schoen, Chee Ping Ng, Kristina Bishard, Manon Haarmans, Sebastian J. Trietsch, Peter Goos","doi":"10.1007/s13253-023-00570-1","DOIUrl":"https://doi.org/10.1007/s13253-023-00570-1","url":null,"abstract":"","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136113871","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}
Pub Date : 2023-10-11DOI: 10.1007/s13253-023-00574-x
Clayton R. Forknall, Arūnas P. Verbyla, Yoni Nazarathy, Adel Yousif, Sarah Osama, Shirley H. Jones, Edward Kerr, Benjamin L. Schulz, Glen P. Fox, Alison M. Kelly
Abstract The size and complexity of datasets resulting from comparative research experiments in the agricultural domain is constantly increasing. Often the number of variables measured in an experiment exceeds the number of experimental units composing the experiment. When there is a necessity to model the covariance relationships that exist between variables in these experiments, estimation difficulties can arise due to the resulting covariance structure being of reduced rank. A statistical method, based in a linear mixed model framework, is presented for the analysis of designed experiments where datasets are characterised by a greater number of variables than experimental units, and for which the modelling of complex covariance structures between variables is desired. Aided by a clustering algorithm, the method enables the estimation of covariance through the introduction of covariance clusters as random effects into the modelling framework, providing an extension of the traditional variance components model for building covariance structures. The method was applied to a multi-phase mass spectrometry-based proteomics experiment, with the aim of exploring changes in the proteome of barley grain over time during the malting process. The modelling approach provides a new linear mixed model-based method for the estimation of covariance structures between variables measured from designed experiments, when there are a small number of experimental units, or observations, informing covariance parameter estimates.
{"title":"Covariance Clustering: Modelling Covariance in Designed Experiments When the Number of Variables is Greater than Experimental Units","authors":"Clayton R. Forknall, Arūnas P. Verbyla, Yoni Nazarathy, Adel Yousif, Sarah Osama, Shirley H. Jones, Edward Kerr, Benjamin L. Schulz, Glen P. Fox, Alison M. Kelly","doi":"10.1007/s13253-023-00574-x","DOIUrl":"https://doi.org/10.1007/s13253-023-00574-x","url":null,"abstract":"Abstract The size and complexity of datasets resulting from comparative research experiments in the agricultural domain is constantly increasing. Often the number of variables measured in an experiment exceeds the number of experimental units composing the experiment. When there is a necessity to model the covariance relationships that exist between variables in these experiments, estimation difficulties can arise due to the resulting covariance structure being of reduced rank. A statistical method, based in a linear mixed model framework, is presented for the analysis of designed experiments where datasets are characterised by a greater number of variables than experimental units, and for which the modelling of complex covariance structures between variables is desired. Aided by a clustering algorithm, the method enables the estimation of covariance through the introduction of covariance clusters as random effects into the modelling framework, providing an extension of the traditional variance components model for building covariance structures. The method was applied to a multi-phase mass spectrometry-based proteomics experiment, with the aim of exploring changes in the proteome of barley grain over time during the malting process. The modelling approach provides a new linear mixed model-based method for the estimation of covariance structures between variables measured from designed experiments, when there are a small number of experimental units, or observations, informing covariance parameter estimates.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136210152","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}
Pub Date : 2023-09-27DOI: 10.1007/s13253-023-00572-z
Babacar Diakhate, Hamza Dhaker, Papa Ngom
{"title":"The $$beta $$-divergence for Bandwidth Selection in Circular Kernel Density Estimation","authors":"Babacar Diakhate, Hamza Dhaker, Papa Ngom","doi":"10.1007/s13253-023-00572-z","DOIUrl":"https://doi.org/10.1007/s13253-023-00572-z","url":null,"abstract":"","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135579263","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}
Pub Date : 2023-09-26DOI: 10.1007/s13253-023-00571-0
André Victor Ribeiro Amaral, Elias Teixeira Krainski, Ruiman Zhong, Paula Moraga
{"title":"Model-Based Geostatistics Under Spatially Varying Preferential Sampling","authors":"André Victor Ribeiro Amaral, Elias Teixeira Krainski, Ruiman Zhong, Paula Moraga","doi":"10.1007/s13253-023-00571-0","DOIUrl":"https://doi.org/10.1007/s13253-023-00571-0","url":null,"abstract":"","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134886241","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}
Pub Date : 2023-09-19DOI: 10.1007/s13253-023-00569-8
Dani Pasaribu, Alrend Roy Peterson Kaputing, Delvianus Kaesmentan
{"title":"Review of “Prediction Techniques for Renewable Energy Generation and Load Demand Forecasting” by Anuradha Tomar, Prerna Gaur, and Xiaolong Jin (Editors)","authors":"Dani Pasaribu, Alrend Roy Peterson Kaputing, Delvianus Kaesmentan","doi":"10.1007/s13253-023-00569-8","DOIUrl":"https://doi.org/10.1007/s13253-023-00569-8","url":null,"abstract":"","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060646","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}
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