Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

11.8K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.8K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27
Scatter Plot01:15

Scatter Plot

6.8K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
6.8K
Distribution and Dispersion00:54

Distribution and Dispersion

21.6K
To understand intra-specific interactions in populations, scientists measure the spatial arrangement of species individuals. This geographic arrangement is known as the species distribution or dispersion. Highly territorial species exhibit a uniform distribution pattern, in which individuals are spaced at relatively equal distances from one another. Species that are highly tied to particular resources, such as food or shelter, tend to concentrate around those resources, and thus exhibit a...
21.6K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

113
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
113
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A construction of statistical inferences in geographically weighted univariate log-gamma regression.

MethodsX·2026
Same author

A multivariate correlated poisson generalized inverse gaussian regression model for dependent count data: Estimation and testing procedures.

MethodsX·2026
Same author

Modified partial least square structural equation model with multivariate adaptive regression spline: Parameter estimation technique and applications.

MethodsX·2025
Same author

Estimation of parameters and hypothesis testing of multivariate spatial autoregressive model.

MethodsX·2025
Same author

Explore poverty with statistical modeling: The bivariate polynomial binary logit regression (BPBLR).

MethodsX·2025
Same author

kppmenet: combining the kppm and elastic net regularization for inhomogeneous Cox point process with correlated covariates.

Journal of applied statistics·2024

相关实验视频

Updated: Jun 14, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

基于地理加权多变量通用性马回归的空间聚类.

Hasbi Yasin1,2, Purhadi1, Achmad Choiruddin1

  • 1Department of Statistics, Institut Teknologi Sepuluh Nopember, Surabaya 60111 Indonesia.

MethodsX
|September 5, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的地理加权多变量通用玛回归 (GWMGGR) 模型,以解决数据中的空间异质性,克服传统GWR模型的正常性假设.

关键词:
教育指标教育指标在GWMGGR中,GWMGGR是GWMGGR.地理加权多变量通用性马回归K-意味着集群的集群.最大的概率比率测试试验空间异质性 空间异质性

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

相关实验视频

Last Updated: Jun 14, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

科学领域:

  • 空间统计的空间统计.
  • 统计建模 统计建模
  • 计量经济学 计量经济学

背景情况:

  • 地理加权回归 (GWR) 模拟空间异质性,但假设正常误差.
  • 现有的GWR模型对于非正常分布的数据是有限的.
  • 多变量连续数据通常表现为非正常分布.

研究的目的:

  • 提出一个新的地理加权多变量通用玛回归 (GWMGGR) 模型.
  • 扩展GWR以处理多变量通用性玛分布式反应.
  • 克服传统GWR中错误的正常性假设.

主要方法:

  • 开发了通用玛分布式反应的GWMGGR模型.
  • 使用BHHH算法进行参数估计的最大概率估计 (MLE).
  • 使用最大概率比率测试 (MLRT) 来测试空间效应的假设.
  • 应用k-means集群用于模型参数的空间解释.

主要成果:

  • GWMGGR模型有效地捕捉了非正常分布数据的空间异质性.
  • 大型空间效应测试 (MLRT) 证实了空间效应在拟议模型中的重要性.
  • 空间聚类揭示了中爪教育指标中明显的模式.

结论:

  • 对于非正常数据,GWMGGR模型为标准GWR提供了一个灵活的替代方案.
  • 该方法在多变量设置中提供了空间异质性的强有力的分析.
  • 该研究展示了GWMGGR在分析区域教育差异方面的实际应用.