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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

600
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
600
Sampling Plans01:23

Sampling Plans

258
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
258
Survival Tree01:19

Survival Tree

159
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...
159
Cluster Sampling Method01:20

Cluster Sampling Method

12.7K
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...
12.7K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.3K
Sampling Distribution01:12

Sampling Distribution

13.6K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
13.6K

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

Optimizing the Use of Proviral DNA HIV Drug Resistance Testing: Clinical Applications and Cautions.

The Journal of infectious diseases·2026
Same author

Optimizing HIV-1 Genotypic Resistance Testing for Low- and Middle-Income Countries: High-Impact HIV-1 Mutations Across WHO-Defined Scenarios.

Viruses·2026
Same author

Study Design, Methods, and Modeling in Networks to Inform HIV Interventions and Policy in Marginalized Populations.

Rhode Island medical journal (2013)·2026
Same author

Letter to the editor: When do rare events become expected in HIV drug resistance?

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin·2026
Same author

Reciprocal repulsions enforce heterotypic dendrite segregation in an olfactory circuit.

bioRxiv : the preprint server for biology·2026
Same author

Longitudinal challenges faced by perinatally-infected young people with HIV in Kenya during the COVID-19 pandemic.

AIDS (London, England)·2026
Same journal

Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

fastkqr: A Fast Algorithm for Kernel Quantile Regression.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Joint Registration and Conformal Prediction for Partially Observed Functional Data.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Efficient Decision Trees for Tensor Regressions.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
Same journal

Distributed Nonparametric Regression with Heterogeneity Through Prediction-Based Aggregation.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America·2026
查看所有相关文章

相关实验视频

Updated: Sep 9, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

多项试点贝叶斯增量回归树的增量采样器

Yizhen Xu1, Joseph Hogan2, Michael Daniels3

  • 1Division of Biostatistics, University of Utah.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|September 2, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了多项试验贝叶斯增量回归树 (MPBART) 的新方法,可以提高马尔科夫链蒙特卡洛 (MCMC) 趋同和预测准确性. 提出的方法为现有的MPBART方法提供了更有效的替代方案.

关键词:
绝对结果数据增强潜伏模式

更多相关视频

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K
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

10.8K

相关实验视频

Last Updated: Sep 9, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.4K
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

10.8K

科学领域:

  • 统计数据
  • 机器学习
  • 计算统计

背景情况:

  • 基于多变量高斯潜伏结构的多项试验 (MNP) 框架,通过不假设独立的替代方案,提供了多项后勤模型的优势.
  • 贝叶斯增量回归树 (BART) 已通过多项试验器BART (MPBART) 集成到MNP中,使用崩的吉布斯采样器进行后端采样.
  • 崩的吉布斯采样器的效率取决于简单的采样步骤和快速的马尔科夫链融合,这可能受到后置树的随机搜索的复杂性所挑战.

研究的目的:

  • 通过提出一个新的后部树采样策略来解决MPBART的计算挑战.
  • 将拟议的方法与现有的MPBART方法进行比较,包括Kindo等. " (2016) 的增强参数空间采样和Sparapani等人. " (2021) 的条件概率规范.
  • 在马尔科夫链蒙特卡洛 (MCMC) 趋同和后期预测准确性方面评估拟议方法的性能.

主要方法:

  • 这项研究建议在受限参数空间的条件下采样后层树,与Kindo等人形成鲜明对比. 使用增强参数空间的方法.
  • 与Sparapani等人进行了比较. " (2021) 方法,该方法使用条件概率来建模多项分布.
  • 使用MCMC融合诊断和后预测准确度指标来评估性能.

主要成果:

  • 拟议的条件抽样方法显示了与条件概率方法相比的MCMC收率和后期预测精度.
  • 这种新方法在MCMC收和预测准确性方面显著优于增强型树采样方法.
  • 理论分析证实,拟议方法的混合率并不低于增强树样采样方法.

结论:

  • 在MPBART中采样后层树的方法提供了更好的计算效率和预测性能.
  • 这种方法为现有的MPBART方法提供了可行的替代方案,特别是优于依赖增强参数空间的方法.
  • 这些发现表明,有条件的抽样策略可以在MNP框架内增强BART的实际应用.