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

484
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...
484
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

420
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
420
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

157
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
157
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

65
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
65
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

239
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
239
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
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...
4.1K

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

Prediction model for children with anaphylaxis who may not require emergency department care: a multicenter retrospective cohort study.

The journal of allergy and clinical immunology. In practice·2026
Same author

Penicillin allergy labels increase risks of MRSA colonization, surgical site infections, and mortality.

Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology·2026
Same author

Association of donor heavy alcohol use with graft failure after deceased-donor liver transplantation stratified by donor sex and macrosteatosis in the OPTN/UNOS registry.

Scientific reports·2026
Same author

Effect of Laser Shock Peening on the Passivation Behavior of Subtractively and Additively Manufactured Ti-6Al-4V Alloys in pH 2 Buffer Solution.

Materials (Basel, Switzerland)·2026
Same author

A longitudinal study on multiple frailty and its associations with depression and social participation in older adults.

Scientific reports·2026
Same author

Transforming nursing education to enhance integrated nursing competency: a Delphi-based methodological study on symptom-based clinical reasoning.

Journal of Korean Academy of Nursing·2026
Same journal

Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Doubly regularized generalized linear models for spatial observations with high-dimensional covariates.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Adaptive Fisher's method using weakly geometric grid for combining <i>p</i>-values with application to COVID-19 surveillance.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Robust domain selection for functional data via interval-wise testing and effect size mapping.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Modelling spatial heterogeneity in exposure buffers and risk: a hierarchical Bayesian approach.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
Same journal

Estimating the causal effects of multiple intermittent treatments with application to COVID-19.

Journal of the Royal Statistical Society. Series C, Applied statistics·2026
查看所有相关文章

相关实验视频

Updated: Jul 23, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

28.4K

对零膨胀多变量计数数据进行贝叶斯非参数分析,并应用于微生物组研究.

Kurtis Shuler1, Samuel Verbanic2, Irene A Chen2

  • 1Sandia National Laboratories in Albuquerque, Albuquerque, NM, USA.

Journal of the Royal Statistical Society. Series C, Applied statistics
|July 13, 2023
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新的贝叶斯非参数 (BNP) 回归模型,用于分析复杂的微生物组数据. 这种先进的模型提高了对微生物群落及其与环境因素的关系的理解.

关键词:
贝叶斯的非参数.取决于迪里克莱特过程.高通量测序的高通量测序微生物组是一个微生物组.多变量计数多变量计数规范化的正常化.运营的分类学单位.零通货膨胀 没有通货膨胀

更多相关视频

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

16.0K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.4K

相关实验视频

Last Updated: Jul 23, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

28.4K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

16.0K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.4K

科学领域:

  • 微生物学 微生物学
  • 统计建模 统计建模
  • 生物信息学是一种生物信息学.

背景情况:

  • 高通量测序产生了来自微生物群落的复杂多变量分类数量数据.
  • 分析这些数据,特别是与共变量相关联的数据,为研究人员带来了重大挑战.
  • 现有的统计方法可能无法完全捕捉微生物群体结构的复杂性.

研究的目的:

  • 开发和评估一种新的贝叶斯非参数 (BNP) 回归模型,用于微生物群计数数据分析.
  • 灵活地建模微生物种群与环境或临床共变量之间的关联.
  • 提供超越传统统计测试的加强社区层面的洞察力.

主要方法:

  • 开发一个零膨胀的贝叶斯非参数 (BNP) 回归模型.
  • 该模型的应用用于分析微生物组研究中的多变量分类数量数据.
  • 通过模拟研究,比较BNP模型与更简单的模型和现有的替代方案.

主要成果:

  • 该BNP模型展示了优越的参数估计和模型适合各种模拟设置.
  • 该模型有效地捕捉了微生物与环境因素和临床特征等共变量之间的关联.
  • 它提供了微生物多样性和差异丰度的概率分布估计,使更深入的社区比较成为可能.

结论:

  • 开发的BNP回归模型为分析复杂的微生物组数据提供了一种强大而灵活的方法.
  • 它增强了对微生物群落组成及其与外部因素的关系的理解.
  • 该模型在现实世界的应用中被证明是有效的,如慢性伤口和人类微生物组项目数据集所示.