Generalized Linear and Nonlinear Mixed Models for Longitudinal and Spatial Data
纵向和空间数据的广义线性和非线性混合模型
基本信息
- 批准号:RGPIN-2015-06124
- 负责人:
- 金额:$ 0.8万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2016
- 资助国家:加拿大
- 起止时间:2016-01-01 至 2017-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The abundance of species over time and space is of great interest in the contemporary ecology, conservation biology, and natural resource management. Observed abundance of different species of plants or animals at the same location is usually negatively correlated in a competitive community, whereas abundance of the same specie tends to be positively correlated because of shared environment and other underlying community characteristics. As the weed community is influenced both directly by fertilizers and indirectly through the effect of crop competition, the correlation is influenced by both shared environmental characteristics and competition. My research interests lie in the development of multivariate random effects models where random effects can be used to account for community effects and the association among neighbouring communities. Our new statistical methods can help the search for community composition patterns of species and their relationship with available environmental characteristics. For example, predicting the spatial distribution of wildlife populations is an important component of the development of management strategies for their conservation. Understanding abundance of species over time and space helps decision makers geographically target resource allocation at appropriate times in an attempt to remediate problem areas.
物种在时间和空间上的丰富度在当代生态学,保护生物学和自然资源管理中具有极大的兴趣。在竞争性群落中,在同一地点观察到的不同植物或动物物种的丰度通常呈负相关,而由于共享的环境和其他潜在的群落特征,同一物种的丰度往往呈正相关。由于杂草群落既受肥料的直接影响,又受作物竞争效应的间接影响,因此这种相关性既受共同的环境特征影响,也受竞争影响。我的研究兴趣在于多变量随机效应模型的发展,其中随机效应可以用来解释社区效应和相邻社区之间的关联。我们新的统计方法可以帮助寻找物种的群落组成模式及其与现有环境特征的关系。例如,预测野生动物种群的空间分布是制定保护野生动物管理战略的一个重要组成部分。了解物种在时间和空间上的丰富程度有助于决策者在适当的时候在地理上确定资源分配的目标,以试图补救问题地区。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Ma, Renjun其他文献
Modeling binomial amphibian roadkill data in distance sampling while accounting for zero-inflation, serial correlation and varying cluster sizes simultaneously
- DOI:
10.1007/s10651-017-0367-1 - 发表时间:
2017-06-01 - 期刊:
- 影响因子:3.8
- 作者:
Hasan, M. Tariqul;Sneddon, Gary;Ma, Renjun - 通讯作者:
Ma, Renjun
Regression analysis of zero-inflated time-series counts: application to air pollution related emergency room visit data
- DOI:
10.1080/02664763.2011.595778 - 发表时间:
2012-01-01 - 期刊:
- 影响因子:1.5
- 作者:
Hasan, M. Tariqul;Sneddon, Gary;Ma, Renjun - 通讯作者:
Ma, Renjun
Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries
- DOI:
10.1016/j.chaos.2020.109829 - 发表时间:
2020-06-01 - 期刊:
- 影响因子:7.8
- 作者:
Zhang, Xiaolei;Ma, Renjun;Wang, Lin - 通讯作者:
Wang, Lin
Forecasting occurrence and quantity of monthly precipitation simultaneously while accounting for complex serial correlation
- DOI:
10.1002/joc.7839 - 发表时间:
2022-09-14 - 期刊:
- 影响因子:0
- 作者:
Duan, Xingde;Ma, Renjun;Zhang, Xiaolei - 通讯作者:
Zhang, Xiaolei
Modelling heterogeneity in clustered count data with extra zeros using compound Poisson random effect
- DOI:
10.1002/sim.3619 - 发表时间:
2009-08-15 - 期刊:
- 影响因子:2
- 作者:
Ma, Renjun;Hasan, M. Tariqul;Sneddon, Gary - 通讯作者:
Sneddon, Gary
Ma, Renjun的其他文献
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{{ truncateString('Ma, Renjun', 18)}}的其他基金
Random Effects Modelling and Inference for Skewed Data of Complex Correlation Structures
复杂相关结构的倾斜数据的随机效应建模和推理
- 批准号:
RGPIN-2020-04751 - 财政年份:2022
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
Random Effects Modelling and Inference for Skewed Data of Complex Correlation Structures
复杂相关结构的倾斜数据的随机效应建模和推理
- 批准号:
RGPIN-2020-04751 - 财政年份:2021
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
Random Effects Modelling and Inference for Skewed Data of Complex Correlation Structures
复杂相关结构的倾斜数据的随机效应建模和推理
- 批准号:
RGPIN-2020-04751 - 财政年份:2020
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
Generalized Linear and Nonlinear Mixed Models for Longitudinal and Spatial Data
纵向和空间数据的广义线性和非线性混合模型
- 批准号:
RGPIN-2015-06124 - 财政年份:2019
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
Generalized Linear and Nonlinear Mixed Models for Longitudinal and Spatial Data
纵向和空间数据的广义线性和非线性混合模型
- 批准号:
RGPIN-2015-06124 - 财政年份:2018
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
Generalized Linear and Nonlinear Mixed Models for Longitudinal and Spatial Data
纵向和空间数据的广义线性和非线性混合模型
- 批准号:
RGPIN-2015-06124 - 财政年份:2017
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
Generalized Linear and Nonlinear Mixed Models for Longitudinal and Spatial Data
纵向和空间数据的广义线性和非线性混合模型
- 批准号:
RGPIN-2015-06124 - 财政年份:2015
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
Random effects regression modeling of spatial and longitudinal data
空间和纵向数据的随机效应回归建模
- 批准号:
238682-2010 - 财政年份:2014
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
Random effects regression modeling of spatial and longitudinal data
空间和纵向数据的随机效应回归建模
- 批准号:
238682-2010 - 财政年份:2013
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
Random effects regression modeling of spatial and longitudinal data
空间和纵向数据的随机效应回归建模
- 批准号:
238682-2010 - 财政年份:2012
- 资助金额:
$ 0.8万 - 项目类别:
Discovery Grants Program - Individual
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