Journal of Agricultural Biological and Environmental Statistics最新文献_第8页

Regularised Semi-parametric Composite Likelihood Intensity Modelling of a Swedish Spatial Ambulance Call Point Pattern 瑞典空间救护车呼叫点模式的正则化半参数复合似然强度建模

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-04-15 DOI: 10.1007/s13253-023-00534-5

Fekadu L. Bayisa, M. Ådahl, Patrik Rydén, O. Cronie

引用次数: 1

Estimation and Clustering of Directional Wave Spectra 方向波谱的估计与聚类

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-04-13 DOI: 10.1007/s13253-023-00543-4

Zihao Wu, Carolina Euán, R. Crujeiras, Yinge Sun

引用次数: 0

Rankıng Districts of Çanakkale in Terms of Rangeland Quality by Fuzzy MCDM Methods 利用模糊MCDM方法对Çanakkale地区的草地质量进行评价

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-04-05 DOI: 10.1007/s13253-023-00532-7

Zeynep Gökkuş, Sevil Sentürk, F. Alatürk

引用次数: 0

A Shared Latent Process Model to Correct for Preferential Sampling in Disease Surveillance Systems 疾病监测系统中优先抽样校正的共享潜伏过程模型

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-04-03 DOI: 10.1007/s13253-023-00535-4

Brian Conroy, L. Waller, Ian D. Buller, Gregory M. Hacker, James R. Tucker, M. Novak

引用次数: 0

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality. 估算连续误差暴露的因果暴露反应函数：细颗粒物与全因死亡率研究》。

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-03-01 Epub Date: 2022-09-11 DOI: 10.1007/s13253-022-00508-z

Kevin P Josey, Priyanka deSouza, Xiao Wu, Danielle Braun, Rachel Nethery

{"title":"Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality.","authors":"Kevin P Josey, Priyanka deSouza, Xiao Wu, Danielle Braun, Rachel Nethery","doi":"10.1007/s13253-022-00508-z","DOIUrl":"10.1007/s13253-022-00508-z","url":null,"abstract":"Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10103900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9693600","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

A Bayesian Partial Membership Model for Multiple Exposures with Uncertain Group Memberships 具有不确定组成员的多曝光贝叶斯部分隶属度模型

IF 1.4 4区数学

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

A. Zavez, E. McSorley, A. Yeates, S. Thurston

引用次数: 0

Fusion Learning of Functional Linear Regression with Application to Genotype-by-Environment Interaction Studies 功能线性回归的融合学习及其在基因型与环境相互作用研究中的应用

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-02-06 DOI: 10.1007/s13253-023-00529-2

Shan Yu, Aaron Kusmec, Li Wang, D. Nettleton

引用次数: 0

Bayesian Latent Variable Co-kriging Model in Remote Sensing for Quality Flagged Observations 遥感质量标记观测贝叶斯潜变量协同克里格模型

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-02-04 DOI: 10.1007/s13253-023-00530-9

B. Konomi, E. Kang, Ayat Almomani, J. Hobbs

引用次数: 2

An Approach for Specifying Trimming and Winsorization Cutoffs 一种指定修剪和加权截止点的方法

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-01-24 DOI: 10.1007/s13253-023-00527-4

Kedai Cheng, D. S. Young

引用次数: 1

Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series. 空间相关函数时间序列的异步变更点估计。

IF 1.4 4区数学

Journal of Agricultural Biological and Environmental Statistics Pub Date : 2023-01-01 DOI: 10.1007/s13253-022-00519-w

Mengchen Wang, Trevor Harris, Bo Li

{"title":"Asynchronous Changepoint Estimation for Spatially Correlated Functional Time Series.","authors":"Mengchen Wang, Trevor Harris, Bo Li","doi":"10.1007/s13253-022-00519-w","DOIUrl":"https://doi.org/10.1007/s13253-022-00519-w","url":null,"abstract":"We propose a new solution under the Bayesian framework to simultaneously estimate mean-based asynchronous changepoints in spatially correlated functional time series. Unlike previous methods that assume a shared changepoint at all spatial locations or ignore spatial correlation, our method treats changepoints as a spatial process. This allows our model to respect spatial heterogeneity and exploit spatial correlations to improve estimation. Our method is derived from the ubiquitous cumulative sum (CUSUM) statistic that dominates changepoint detection in functional time series. However, instead of directly searching for the maximum of the CUSUM-based processes, we build spatially correlated two-piece linear models with appropriate variance structure to locate all changepoints at once. The proposed linear model approach increases the robustness of our method to variability in the CUSUM process, which, combined with our spatial correlation model, improves changepoint estimation near the edges. We demonstrate through extensive simulation studies that our method outperforms existing functional changepoint estimators in terms of both estimation accuracy and uncertainty quantification, under either weak or strong spatial correlation, and weak or strong change signals. Finally, we demonstrate our method using a temperature data set and a coronavirus disease 2019 (COVID-19) study. Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00519-w.","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10687024","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: 2