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

Distribution Reliability and Automation01:25

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
Reliability and Validity01:29

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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

Estimating Population Mean with Unknown Standard Deviation

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 Guinness...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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...

You might also read

Related Articles

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

Sort by
Same author

UK multisociety consensus statement on the emergency management and resuscitation of patients with left-sided Impella support.

Heart (British Cardiac Society)·2025
Same author

Assessing the chemical burden of the North-East Atlantic ecosystem through targeted and untargeted HRMS-based approaches.

Journal of hazardous materials·2025
Same author

Cross-cutting studies of per- and polyfluorinated alkyl substances (PFAS) in Arctic wildlife and humans.

The Science of the total environment·2024
Same author

Global mercury concentrations in biota: their use as a basis for a global biomonitoring framework.

Ecotoxicology (London, England)·2024
Same author

The Fixated Threat Assessment Centre.

The Medico-legal journal·2024
Same author

Progress in the discovery of isopods (Crustacea: Peracarida)-is the description rate slowing down?

PeerJ·2023
Same journal

Shared frailty sieve estimation for dependent left truncated and interval censored data.

Lifetime data analysis·2026
Same journal

Functional win-fractions regression models for composite outcomes.

Lifetime data analysis·2026
Same journal

Variable selection in causal semiparametric transformation models with all-or-nothing treatment compliance.

Lifetime data analysis·2026
Same journal

Correction to: A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model.

Lifetime data analysis·2026
Same journal

Unobserved heterogeneity in threshold regression based on the hitting times of a reflected Brownian motion for recurrent hypoglycemia.

Lifetime data analysis·2026
Same journal

Variable selection with broken adaptive ridge regression for interval-censored competing risks data.

Lifetime data analysis·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2026

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
09:55

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

Reliability estimation from field return data.

Simon Wilson1, Toby Joyce, Ed Lisay

  • 1Centre for Telecommunications Value-Chain Research, Department of Statistics, Trinity College Dublin, Dublin 2, Ireland. simon.wilson@tcd.ie

Lifetime Data Analysis
|May 5, 2009
PubMed
Summary
This summary is machine-generated.

This study develops statistical inference methods to determine product failure times from field return data. It accurately estimates time-to-failure distributions by accounting for shipping and return durations.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

Related Experiment Videos

Last Updated: Jun 23, 2026

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
09:55

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

Area of Science:

  • Reliability Engineering
  • Statistical Modeling
  • Product Lifecycle Management

Background:

  • Field return data often includes non-failure time intervals, complicating accurate failure time analysis.
  • Accurate assessment of product reliability is crucial for effective maintenance and warranty management.
  • Existing methods may not adequately isolate true failure times from logistical delays.

Purpose of the Study:

  • To develop a statistical inference method for estimating product failure time distributions using field return data.
  • To address the challenge of non-failure time components within field return data.
  • To provide a robust approach for analyzing product reliability in telecommunications networks.

Main Methods:

  • Utilizing statistical inference techniques to model failure time distributions.
  • Incorporating separate data on shipping, installation, and return times.
  • Applying the developed method to field return data from telecommunications network units.

Main Results:

  • Successfully inferred product failure time distributions from composite field return data.
  • Demonstrated the method's efficacy in isolating true time-to-failure from logistical delays.
  • Provided a practical framework for reliability analysis in real-world telecommunications deployments.

Conclusions:

  • The proposed statistical inference method accurately estimates product failure time distributions.
  • Accounting for shipping and return times is essential for precise reliability assessments.
  • This approach enhances product lifecycle management and predictive maintenance strategies.