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

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

1.3K
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...
1.3K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.9K
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...
8.9K
Multiple Regression01:25

Multiple Regression

4.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.2K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.2K
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...
5.2K
Regression Toward the Mean01:52

Regression Toward the Mean

7.2K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.2K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.7K
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 +...
9.7K

You might also read

Related Articles

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

Sort by
Same author

Stability-driven multi-omics integration for reproducible latent structure.

bioRxiv : the preprint server for biology·2026
Same author

A Reporting Guideline for Observational Studies in Metabolomic Epidemiology: Explanation and Elaboration of the Strobe-MetEpi Checklist.

Research square·2026
Same author

Maternal lifetime stress and placental mitochondrial DNA mutational load: Effect modification by maternal characteristics.

Placenta·2026
Same author

Exposure to coal-fired power plant emissions, unconventional natural gas development, and salivary miRNA profiles and asthma in children.

Environmental research·2026
Same author

Prenatal humid heat exposure and cord blood extracellular vesicle microRNA profiles in a Ghanaian Pregnancy Cohort: GRAPHS.

Environmental epigenetics·2026
Same author

Racial and ethnic differences in the association between pediatric phthalate exposures and antibody response to vaccination from NHANES 2015-2016.

Environmental research·2026

Related Experiment Video

Updated: Feb 26, 2026

Methodology for Accurate Detection of Mitochondrial DNA Methylation
12:11

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

14.0K

Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation

Haixiang Zhang1, Yinan Zheng1, Grace Yoon1

  • 1.

Statistical Applications in Genetics and Molecular Biology
|July 23, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for selecting important DNA methylation markers from high-dimensional data. The approach simplifies computations and effectively handles correlated outcomes, aiding in biomarker discovery.

Keywords:
high-dimensional responsesmultivariate regressionoracle inequalitytuning-insensitiveweighted square-root LASSO

More Related Videos

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

5.3K
Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
07:50

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

6.2K

Related Experiment Videos

Last Updated: Feb 26, 2026

Methodology for Accurate Detection of Mitochondrial DNA Methylation
12:11

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

14.0K
Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

5.3K
Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
07:50

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

6.2K

Area of Science:

  • Genomics
  • Biostatistics
  • Epigenetics

Background:

  • High-dimensional DNA methylation data presents challenges for variable selection.
  • Correlated multivariate outcomes require specialized statistical methods.
  • Existing methods may involve computationally intensive cross-validation.

Purpose of the Study:

  • To propose a novel weighted square-root LASSO procedure for variable selection in high-dimensional correlated DNA methylation markers.
  • To develop a computationally efficient method that is insensitive to tuning parameters.
  • To account for within-subject correlations using a precision matrix.

Main Methods:

  • A weighted square-root LASSO procedure is proposed for estimating the regression coefficient matrix.
  • Constrained L1 minimization is used to obtain a precision matrix for handling correlations.
  • Oracle inequalities are derived for the regularized estimators.

Main Results:

  • The proposed method demonstrates effective variable selection for correlated high-dimensional data.
  • Tuning-insensitivity simplifies the computational process by removing the need for cross-validation.
  • Simulations confirm the method's strong performance.

Conclusions:

  • The novel weighted square-root LASSO procedure offers an efficient and effective approach for analyzing high-dimensional DNA methylation data.
  • The method is applicable to complex biological datasets, such as those investigating smoking and methylation in aging studies.
  • This work contributes to advancing statistical methodologies in epigenetics research.