Search research articles

Search research articles

Related Experiment Videos

Inference for reliability and stress-strength for a scaled Burr type X distribution.

J G Surles¹, W J Padgett

¹Department of Mathematics and Statistics, Texas Tech University, Lubbock, Texas 79409, USA. surles@math.ttu.edu

Lifetime Data Analysis

|July 19, 2001

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Accelerated degradation models for failure based on geometric Brownian motion and gamma processes.

Lifetime data analysis·2005

Same author

Inference from accelerated degradation and failure data based on Gaussian process models.

Lifetime data analysis·2004

Same author

Accelerated test models for system strength based on Birnbaum-Saunders distributions.

Lifetime data analysis·1999

Same author

Families of smooth confidence bands for the survival function under the general random censorship model.

Lifetime data analysis·1996

Same author

A stochastic model for chemotherapy: two-organ systems.

International journal of bio-medical computing·1972

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study develops new statistical methods for reliability analysis when data follows a Burr type X distribution. These techniques help estimate the probability of failure (R) in engineering applications.

Area of Science:

Statistics
Probability Theory
Reliability Engineering

Background:

Reliability analysis is crucial in engineering.
The Burr type X distribution is used for modeling.
Exact inference for reliability (R) is often challenging.

Purpose of the Study:

To develop statistical inference procedures for R = P(Y < X) under the Burr type X model.
To address the lack of exact inference methods for this probability.
To provide practical tools for reliability estimation.

Main Methods:

Derivation of the expected Fisher information matrix.
Development of asymptotic inference procedures for R and general functions of parameters.
Application of a bootstrap method for estimating variance of maximum likelihood estimators.

Related Experiment Videos

Main Results:

Asymptotic inference procedures for R were successfully developed.
The expected Fisher information matrix was derived.
Bootstrap method proposed for variance estimation.

Conclusions:

The developed asymptotic and bootstrap methods offer effective solutions for reliability inference.
These techniques are applicable to real-world data, such as carbon fiber strength.
Simulations confirm the practical utility of the proposed methods.