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

Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

305
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
305
Survival Tree01:19

Survival Tree

75
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
75
Correlation of Experimental Data01:23

Correlation of Experimental Data

229
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
229
Residual Plots01:07

Residual Plots

4.6K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
4.6K

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

Ambient Air Pollution and COVID-19 in California.

Research report (Health Effects Institute)·2026
Same author

Associations between classroom ventilation rates and school characteristics with indoor air in classrooms during wildfire smoke events.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Is greenspace in the eye of the beholder? Exploring perceived and objective greenspace exposure effects on mental health.

Journal of environmental psychology·2025
Same author

Effect modification of the association between fine particulate air pollution during a wildfire event and respiratory health by area-level measures of socio-economic status, race/ethnicity, and smoking prevalence.

Environmental research, health : ERH·2024
Same author

Social Susceptibility to Multiple Air Pollutants in Cardiovascular Disease.

Research report (Health Effects Institute)·2022
Same author

Improvements in Air Quality and Health Outcomes Among California Medicaid Enrollees Due to Goods Movement Actions.

Research report (Health Effects Institute)·2022

相关实验视频

Updated: Jun 18, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K

对于时空数据的预测和模型评估.

G L Watson1, C E Reid2, M Jerrett3

  • 1Department of Biostatistics, University of California, Los Angeles, CA, USA.

Journal of applied statistics
|July 29, 2024
PubMed
概括
此摘要是机器生成的。

对时空数据进行准确的预测错误评估具有挑战性. 推基于位置的交叉验证,特别是离开一个位置的交叉验证 (LOLO),用于空间插值错误估计.

关键词:
通过交叉验证验证.概括错误是一般化的错误.机器学习是机器学习.过程中的点点过程.空间时间数据数据.

更多相关视频

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

相关实验视频

Last Updated: Jun 18, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.5K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

科学领域:

  • 环境科学 环境科学
  • 统计 统计 统计 统计
  • 地理空间分析的研究.

背景情况:

  • 在时空数据中预测错误的评估指标被人们理解得很少.
  • 独立复制通常不存在,这使得独立数据的标准评估程序不适合时空预测.
  • 2008年加利福尼亚州野火造成的空气污染数据凸显了对强大的空间插值误差指标的需求.

研究的目的:

  • 为了正式化空间插值的真预测误差.
  • 调查各种交叉验证 (CV) 程序,以估计时空数据中的预测错误.
  • 为准确的错误估计提供对数据分区策略的见解.

主要方法:

  • 使用模拟和案例研究来分析不同的交叉验证 (CV) 策略.
  • 专注于空间插值错误估计.
  • 评估了基于位置的CV程序的适用性.

主要成果:

  • 基于位置的交叉验证适用于估计空间插值错误,正如加利福尼亚州野火空气污染数据所示.
  • 关于CV折叠大小偏差差异权衡的普遍信念并不直接适用于依赖的时空数据.
  • 离开一个位置的CV (LOLO) 成为空间插值预测错误的首选指标.

结论:

  • 已建立基于位置的交叉验证作为空间插值错误评估的合适方法.
  • 突出了对依赖时空数据的传统CV方法的局限性.
  • 推的LOLO交叉验证用于空间插值任务中准确的预测错误指标.