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

2.3K
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. 
2.3K
Biostatistics: Overview01:20

Biostatistics: Overview

250
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
250
Survival Tree01:19

Survival Tree

87
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...
87
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.4K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.4K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.7K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
5.7K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K

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

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same author

Monte Carlo inference for semiparametric Bayesian regression.

Journal of the American Statistical Association·2025
Same author

Spatial Variability in Relationships between Early Childhood Lead Exposure and Standardized Test Scores in Fourth Grade North Carolina Public School Students (2013-2016).

Environmental health perspectives·2024
Same author

Evaluating integration of letter fragments through contrast and spatially targeted masking.

Journal of vision·2024
Same author

Regression with race-modifiers: towards equity and interpretability.

Research square·2024
Same author

Regression with race-modifiers: towards equity and interpretability.

medRxiv : the preprint server for health sciences·2024
Same journal

Classification Under Local Differential Privacy with Model Reversal and Model Averaging.

Journal of machine learning research : JMLR·2026
Same journal

Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data.

Journal of machine learning research : JMLR·2026
Same journal

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.

Journal of machine learning research : JMLR·2026
Same journal

Unsupervised Tree Boosting for Learning Probability Distributions.

Journal of machine learning research : JMLR·2026
Same journal

A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations.

Journal of machine learning research : JMLR·2026
Same journal

Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes.

Journal of machine learning research : JMLR·2026
查看所有相关文章

相关实验视频

Updated: Jul 8, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K

贝叶斯的子集选择和可解释预测和分类的变量重要性.

Daniel R Kowal1

  • 1Department of Statistics, Rice University, Houston, TX 77005, USA.

Journal of machine learning research : JMLR
|December 18, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了贝叶斯的子集选择方法,为预测建模提供稳定和可解释的变量子集. 该方法识别了多个近最佳子集,改善了预测和变量重要性洞察力.

关键词:
教育教育教育教育的教育.线性回归是一种线性回归.逻辑回归的逻辑回归模型选择,模型选择.受到惩罚的回归回归.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

763
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

相关实验视频

Last Updated: Jul 8, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.5K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

763
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 经典的子集选择方法存在不稳定性和缺乏规范化.
  • 可解释性学习,科学发现和数据压缩从子集选择中受益.
  • 贝叶斯方法提供了一个强大的框架来解决传统子集选择的局限性.

研究的目的:

  • 开发一个贝叶斯框架来提取近最佳变量子集的家族.
  • 为模型解释和变量重要性指标提供新的途径.
  • 导出任何子集的最佳线性系数,包括规范化和不确定性量化.

主要方法:

  • 使用贝叶斯预测模型提取一个"可接受的家族"的子集.
  • 应用贝叶斯决策分析来推导选定的子集的最佳线性系数.
  • 评估对模拟和真实世界的数据的方法,包括一个大型的教育数据集.

主要成果:

  • 与现有方法相比,拟议的贝叶斯子集选择证明了优越的预测,间隔估计和变量选择.
  • 在教育数据集上识别了200多个不同的子集,具有近乎最佳的样本外预测准确性.
  • 开发了基于子集纳入的变量重要性的新型指标.

结论:

  • 贝叶斯框架有效地解决了经典子集选择中的挑战,提供了稳定性和可解释性.
  • "可接受的家庭"概念和衍生的指标提供了增强的模型理解.
  • 该方法在复杂数据集的预测性能和变量选择方面取得了显著的改进.