Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.5K
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...
4.5K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

855
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
855
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.4K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.4K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

153
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
153
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.1K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.1K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.1K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Analysis of Twitter data with the Bayesian fused graphical lasso.

PloS one·2020
Same author

Ridge estimation of network models from time-course omics data.

Biometrical journal. Biometrische Zeitschrift·2018
Same author

Reconstruction of molecular network evolution from cross-sectional omics data.

Biometrical journal. Biometrische Zeitschrift·2018
Same author

Efficacy of high-intensity focused ultrasound-assisted hepatic resection (HIFU-AR) on blood loss reduction in patients with liver metastases requiring hepatectomy: study protocol for a randomized controlled trial.

Trials·2017
Same author

Sphere-Induced Rejuvenation of Swine and Human Müller Glia Is Primarily Caused by Telomere Elongation.

Stem cells (Dayton, Ohio)·2017
Same author

Selenomethionine Mitigates Cognitive Decline by Targeting Both Tau Hyperphosphorylation and Autophagic Clearance in an Alzheimer's Disease Mouse Model.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2017

Related Experiment Video

Updated: Nov 5, 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

3.5K

Penalized estimation of the Gaussian graphical model from data with replicates.

Wessel N van Wieringen1,2, Yao Chen3

  • 1Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands.

Statistics in Medicine
|May 14, 2021
PubMed
Summary

Replicated data improve Gaussian graphical models by separating signal from noise. This study introduces a penalized Expectation-Maximization algorithm for accurate conditional independence graph reconstruction, crucial for biological network analysis.

Keywords:
conditional independence graphinverse covariancenetworkreproducibilityridge penalty

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K

Related Experiment Videos

Last Updated: Nov 5, 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

3.5K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.7K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K

Area of Science:

  • Statistics
  • Computational Biology
  • Genomics

Background:

  • Gaussian graphical models (GGMs) are typically estimated from unreplicated data, conflating signal and noise.
  • Ignoring noise in unreplicated data leads to inaccurate reconstruction of conditional independence graphs.
  • Replicated data offer a solution for deconvolution and accurate graph inference.

Purpose of the Study:

  • To highlight the impact of ignoring noise in unreplicated data on GGM inference.
  • To present a novel penalized Expectation-Maximization algorithm for GGMs using replicated data.
  • To reconstruct accurate conditional independence graphs (gene-gene interaction networks) from replicated oncogenomic data.

Main Methods:

  • A penalized Expectation-Maximization algorithm is proposed for GGM estimation with replicated data.
  • The penalty parameter is optimized using F-fold cross-validation of the log-likelihood.
  • Simulation studies and an oncogenomic data application are used for evaluation.

Main Results:

  • Replicated data significantly improve the reconstruction of the signal's conditional independence graph.
  • The proposed method outperforms competitors in simulations.
  • Application to oncogenomic data reveals realistic gene-gene interaction networks.

Conclusions:

  • Utilizing replicated data is essential for accurate GGM inference and conditional independence graph reconstruction.
  • The penalized Expectation-Maximization algorithm provides a robust method for analyzing such data.
  • Findings have implications for the reproducibility of gene-gene interaction networks in biological research.