Journal of Agricultural Biological and Environmental Statistics最新文献_第5页

3D Point Cloud Semantic Segmentation Through Functional Data Analysis 基于功能数据分析的三维点云语义分割

4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-09-12 DOI: 10.1007/s13253-023-00567-w

Manuel Oviedo de la Fuente, Carlos Cabo, Javier Roca-Pardiñas, E. Louise Loudermilk, Celestino Ordóñez

{"title":"3D Point Cloud Semantic Segmentation Through Functional Data Analysis","authors":"Manuel Oviedo de la Fuente, Carlos Cabo, Javier Roca-Pardiñas, E. Louise Loudermilk, Celestino Ordóñez","doi":"10.1007/s13253-023-00567-w","DOIUrl":"https://doi.org/10.1007/s13253-023-00567-w","url":null,"abstract":"Abstract Here, we propose a method for the semantic segmentation of 3D point clouds based on functional data analysis. For each point of a training set, a number of handcrafted features representing the local geometry around it are calculated at different scales, that is, varying the spatial extension of the local analysis. Calculating the scales at small intervals allows each feature to be accurately approximated using a smooth function and, for the problem of semantic segmentation, to be tackled using functional data analysis. We also present a step-wise method to select the optimal features to include in the model based on the calculation of the distance correlation between each feature and the response variable. The algorithm showed promising results when applied to simulated data. When applied to the semantic segmentation of a point cloud of a forested plot, the results proved better than when using a standard multiscale semantic segmentation method. The comparison with two popular deep learning models showed that our proposal requires smaller training samples sizes and that it can compete with these methods in terms of prediction.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135826880","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

J. A. Diehl and H. Kaur (Eds.): New Forms of Urban Agriculture: An Urban Ecology Perspective—A Book Review J. A. Diehl和H. Kaur(编):都市农业的新形式:城市生态学的视角——书评

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-09-05 DOI: 10.1007/s13253-023-00568-9

Prodipto Bishnu Angon

引用次数: 0

A Bayesian Partial Membership Model for Multiple Exposures with Uncertain Group Memberships. 针对具有不确定群体成员资格的多重暴露的贝叶斯部分成员资格模型。

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-09-01 Epub Date: 2023-02-14 DOI: 10.1007/s13253-023-00528-3

Alexis E Zavez, Emeir M McSorley, Alison J Yeates, Sally W Thurston

{"title":"A Bayesian Partial Membership Model for Multiple Exposures with Uncertain Group Memberships.","authors":"Alexis E Zavez, Emeir M McSorley, Alison J Yeates, Sally W Thurston","doi":"10.1007/s13253-023-00528-3","DOIUrl":"10.1007/s13253-023-00528-3","url":null,"abstract":"We present a Bayesian partial membership model that estimates the associations between an outcome, a small number of latent variables, and multiple observed exposures where the number of latent variables is specified a priori. We assign one observed exposure as the sentinel marker for each latent variable. The model allows non-sentinel exposures to have complete membership in one latent group, or partial membership across two or more latent groups. MCMC sampling is used to determine latent group partial memberships for the non-sentinel exposures, and estimate all model parameters. We compare the performance of our model to competing approaches in a simulation study and apply our model to inflammatory marker data measured in a large mother-child cohort of the Seychelles Child Development Study (SCDS). In simulations, our model estimated model parameters with little bias, adequate coverage, and tighter credible intervals compared to competing approaches. Under our partial membership model with two latent groups, SCDS inflammatory marker classifications generally aligned with the scientific literature. Incorporating additional SCDS inflammatory markers and more latent groups produced similar groupings of markers that also aligned with the literature. Associations between covariates and birth weight were similar across latent variable models and were consistent with earlier work in this SCDS cohort.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11530226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80008642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clustered and Unclustered Group Testing for Biosecurity 生物安全的聚类和非聚类组测试

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-08-26 DOI: 10.1007/s13253-023-00566-x

R. Clark, B. Barnes, M. Parsa

引用次数: 0

Environmental Public Policy Making Exposed, Cynthia H. Stahl, Alan J. Cimorelli, Switzerland: Springer Nature Switzerland AG (2020). 203 pp, ISBN 978-3-030–32130-7 (eBook) 《环境公共政策制定》，辛西娅·h·斯塔尔，艾伦·j·西莫雷利，瑞士:施普林格·自然瑞士股份有限公司(2020)。203 pp, ISBN 978-3-030-32130-7(电子书)

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-08-26 DOI: 10.1007/s13253-023-00556-z

Edyanto, L. Arifin, Syaharuddin

引用次数: 0

A Nonparametric Bootstrap Method for Heteroscedastic Functional Data 异方差泛函数据的非参数自举法

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-08-16 DOI: 10.1007/s13253-023-00561-2

R. Fernández-Casal, Sergio Castillo-Páez, Miguel Flores

引用次数: 0

An Application of Spatio-temporal Modeling to Finite Population Abundance Prediction. 时空建模在有限种群丰度预测中的应用。

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-08-07 DOI: 10.1007/s13253-023-00565-y

Matt Higham, Michael Dumelle, Carly Hammond, Jay Ver Hoef, Jeff Wells

{"title":"An Application of Spatio-temporal Modeling to Finite Population Abundance Prediction.","authors":"Matt Higham, Michael Dumelle, Carly Hammond, Jay Ver Hoef, Jeff Wells","doi":"10.1007/s13253-023-00565-y","DOIUrl":"10.1007/s13253-023-00565-y","url":null,"abstract":"Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be a lack of resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations. This ST-FPBK predictor incorporates an appropriate variance reduction for sampling from a finite population. Through an application to moose surveys in the east-central region of Alaska, we show that the predictor has a substantially smaller standard error compared to a predictor from the purely spatial model that is currently used to analyze moose surveys in the region. We also show how the model can be used to forecast a prediction for abundance in a time point for which spatial locations have not yet been surveyed. A separate simulation study shows that the spatio-temporal predictor is unbiased and that prediction intervals from the ST-FPBK predictor attain appropriate coverage. For ecological monitoring surveys completed with some regularity through time, use of ST-FPBK could improve precision. We also give an R package that ecologists and resource managers could use to incorporate data from past surveys in predicting a quantity from a current survey.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimator of Agreement with Covariate Adjustment 协变量调整的一致性估计

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-08-05 DOI: 10.1007/s13253-023-00553-2

Katelyn A. McKenzie, J. Mahnken

引用次数: 0

Spatially Clustered Survey Designs 空间聚类调查设计

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-07-28 DOI: 10.1007/s13253-023-00562-1

S. Foster, E. Lawrence, A. Hoskins

引用次数: 0

Presence-Only for Marked Point Process Under Preferential Sampling 优先抽样下标记点过程的只存在性

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-07-26 DOI: 10.1007/s13253-023-00558-x

Guido A. Moreira, R. Menezes, L. Wise

引用次数: 0