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

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

95.2K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
95.2K
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
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.0K
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 +...
9.0K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

6.4K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
6.4K
Random and Systematic Errors01:20

Random and Systematic Errors

13.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
13.2K
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

You might also read

Related Articles

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

Sort by
Same author

Leptin in metabolic-immune crosstalk: Mechanisms of T cell regulation and implications for autoimmune disease therapy.

International reviews of immunology·2026
Same author

PLLA@PDA-DOX Nanobubbles for Ultrasound Imaging Combined Chemo-Photothermal Therapy.

Biomolecules·2026
Same author

Enhanced Antibacterial and Immunomodulatory Porphyrin-Based MOF Coatings for PETG Clear Aligners: A Comparative Study of Ag, Cu, and Ce Metal Centers.

International journal of molecular sciences·2026
Same author

Hypofibrinogenemia caused by a heterozygous variant in the FGA gene: a case report.

Thrombosis journal·2026
Same author

Restriction-modification systems and prophages drive genomic diversification in Priestia megaterium.

Biology direct·2026
Same author

Acid Degradation, Structure Characterization of a Novel Polysaccharide from Leaves of <i>Isatis indigotica</i> Fort. with Immunomodulatory Activity.

Molecules (Basel, Switzerland)·2026
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 6, 2025

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.4K

Sparse Density Estimation with Measurement Errors.

Xiaowei Yang1, Huiming Zhang2,3, Haoyu Wei4

  • 1School of Mathematics and Statistics, Chaohu University, Hefei 238000, China.

Entropy (Basel, Switzerland)
|January 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a corrected sparse density estimator (CSDE) to accurately estimate data densities with measurement errors. The novel method improves accuracy and outperforms conventional approaches, particularly for complex density shapes.

Keywords:
Elastic-netdensity estimationmeasurement errorsmulti-mode datasupport recovery

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.2K

Related Experiment Videos

Last Updated: Oct 6, 2025

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
09:32

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

Published on: November 20, 2017

9.4K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.2K

Area of Science:

  • Statistical Inference
  • Machine Learning
  • Data Science

Background:

  • Estimating data density with measurement errors is challenging.
  • Existing methods may lack accuracy for complex distributions.

Purpose of the Study:

  • Propose and investigate a corrected sparse density estimator (CSDE).
  • Estimate unknown data densities using a penalized minimal ℓ2-distance method.

Main Methods:

  • Employed a weighted Elastic-net penalized minimal ℓ2-distance approach.
  • Utilized adaptive weights derived from concentration inequalities.
  • Derived non-asymptotic oracle inequalities under specific assumptions.

Main Results:

  • Achieved optimal weighted tuning parameters with high probability.
  • Demonstrated high-probability support recovery.
  • Numerical experiments showed significant improvement over conventional methods.

Conclusions:

  • The CSDE offers superior performance in density estimation with measurement errors.
  • The method excels at detecting multi-mode density shapes, as shown in a meteorology dataset application.