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

Censoring Survival Data01:09

Censoring Survival Data

645
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
645
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

310
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...
310
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

581
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
581
Poisson Probability Distribution01:09

Poisson Probability Distribution

12.4K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
12.4K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.7K
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...
5.7K

You might also read

Related Articles

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

Sort by
Same author

Lycopene inhibits airway remodeling in allergic asthma models by suppressing the epithelial-mesenchymal transformation in airway epithelial cells.

European journal of nutrition·2026
Same author

Effective-Component Compatibility of Bufei Yishen Formula Alleviates Alveolar Epithelial Barrier Damage in COPD Through Inhibition of p38 MAPK Phosphorylation.

International journal of chronic obstructive pulmonary disease·2026
Same author

Integrated genomic and immunophenotypic profiling reveals monoclonal origin, smoking-driven evolution and heterogeneous microenvironment in pulmonary adenosquamous carcinoma.

Frontiers in immunology·2026
Same author

Recovery of Platinum Group Metals from Spent Automotive Catalysts: A Review of Processes and Challenges.

Materials (Basel, Switzerland)·2026
Same author

Localizing the Epileptogenic Zone Using SEEG-Based Excitation-Inhibition Dynamics and Spectral Features in Drug-Resistant Epilepsy: A Multicenter Retrospective Study.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Ultrasonic treatment effects on hydroxyl radical generation in various solution systems: an iodometric analysis.

Ultrasonics sonochemistry·2026

Related Experiment Video

Updated: Mar 29, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K

Classical and Bayesian Inference for the Two-Parameter Rayleigh Distribution with Random Censored Data.

Lanxi Zhang1, Wenhao Gui1, Zihan Zhao1

  • 1School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China.

Entropy (Basel, Switzerland)
|March 28, 2026
PubMed
Summary

A new two-parameter Rayleigh distribution model improves parameter estimation and reliability analysis for censored data. This enhanced model accurately captures threshold characteristics, outperforming the single-parameter version in real-world applications.

Keywords:
Bayes estimationrandom censoringreliability analysissurvival analysis

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Related Experiment Videos

Last Updated: Mar 29, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Area of Science:

  • Statistics
  • Reliability Engineering
  • Survival Analysis

Background:

  • The standard Rayleigh distribution has limitations in parameter estimation, especially with high minimum values in censored data.
  • A single-parameter Rayleigh distribution lacks a threshold parameter crucial for many practical applications.

Purpose of the Study:

  • To propose and evaluate a two-parameter Rayleigh distribution model for parameter estimation and reliability analysis under random censoring.
  • To address the limitations of the conventional single-parameter Rayleigh distribution in handling threshold characteristics.

Main Methods:

  • Development of a randomly censored data model.
  • Derivation of classical inference methods, including maximum likelihood estimation (MLE).
  • Construction of a Bayesian estimation framework and analysis of reliability characteristics.

Main Results:

  • The two-parameter Rayleigh distribution demonstrates superior performance in parameter estimation and reliability analysis compared to the single-parameter model.
  • Monte Carlo simulations confirm the effectiveness of the proposed estimators.
  • Validation using real strength datasets confirms the model's practicality and superiority.

Conclusions:

  • The two-parameter Rayleigh distribution provides a more accurate description of survival data with threshold characteristics.
  • The proposed model offers improved model fit and reliability estimation for censored data.