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

699
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...
699
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
What are Estimates?01:06

What are Estimates?

5.4K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
5.4K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

8.9K
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 +...
8.9K
Regression Toward the Mean01:52

Regression Toward the Mean

6.5K
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...
6.5K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
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...
8.3K

You might also read

Related Articles

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

Sort by
Same author

Targeting C/EBPβ/SCD1/C16:1 loop-driven macrophage M2 polarization by Bruceine D improves anti-PD-1 immunotherapy in colorectal cancer.

Cancer letters·2026
Same author

QD394 induces ferroptosis and suppresses the proliferation of colorectal cancer via the SP1/JNK pathway.

Apoptosis : an international journal on programmed cell death·2026
Same author

Synergistic Defect Passivation via Multiple Effects for High-Efficiency and Stable Perovskite Solar Cells.

ChemSusChem·2026
Same author

SREBP1 knockdown triggers ferroptosis by suppressing the Nrf2-XCT/GPX4 axis in ovarian cancer.

Cell death discovery·2026
Same author

Ferroptosis in lung cancer: Emerging mechanisms, therapeutic targeting, and immune modulation.

Cellular signalling·2026
Same author

Targeted immunotherapies and nanomedicines for ovarian cancer: the way forward.

NPJ precision oncology·2026
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: Sep 6, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K

Mean and Covariance Estimation for Functional Snippets.

Zhenhua Lin1, Jane-Ling Wang2

  • 1Department of Statistics and Applied Probability National University of Singapore.

Journal of the American Statistical Association
|June 27, 2022
PubMed
Summary
This summary is machine-generated.

Estimating covariance for functional snippets is challenging due to missing data. A new hybrid method decomposes covariance into variance and correlation components, improving estimation accuracy for functional data analysis.

Keywords:
Functional data analysiscorrelation functionfunctional principal component analysissparse functional datavariance function

More Related Videos

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.3K
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

Related Experiment Videos

Last Updated: Sep 6, 2025

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K
Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools
11:29

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

9.3K
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

Area of Science:

  • Statistics
  • Functional Data Analysis

Background:

  • Functional data analysis often involves analyzing short segments of functions (functional snippets).
  • Estimating covariance functions for these snippets is difficult due to missing information, especially in off-diagonal regions.

Purpose of the Study:

  • To develop an effective method for estimating mean and covariance functions of functional snippets.
  • To address the challenge of missing information in the covariance structure of functional snippets.

Main Methods:

  • Decomposing the covariance function into a variance function (estimated nonparametrically) and a correlation function (modeled parametrically).
  • Proposing and analyzing a new estimator for the variance of measurement errors.

Main Results:

  • The hybrid strategy effectively estimates covariance functions for functional snippets.
  • Theoretical analysis and numerical simulations confirm the hybrid strategy's effectiveness.
  • The new measurement error variance estimator possesses desirable asymptotic properties.

Conclusions:

  • The proposed hybrid approach offers a robust solution for estimating covariance in functional snippets.
  • Accurate estimation of variance functions from noisy data is facilitated by the new measurement error estimator.