bibs (version 1.1.1)

Jeffreysbs: Computing the Bayesian estimators of the Birnbaum-Saunders (BS) distribution.

Description

Computing the Bayesian estimators of the BS distribution based on approximated Jeffreys prior proposed by Achcar (1993). The approximated Jeffreys piors is \(\pi_{j}(\alpha,\beta)\propto\frac{1}{\alpha\beta}\sqrt{\frac{1}{\alpha^2}+\frac{1}{4}}\).

Usage

Jeffreysbs(x, CI = 0.95, M0 = 800, M = 1000)

Arguments

x

Vector of observations.

CI

Confidence level for constructing percentile and asymptotic confidence intervals. That is 0.95 by default.

M0

The number of sampler runs considered as burn-in.

M

The number of total sampler runs.

Value

A list including summary statistics of a Gibbs sampler for the Bayesian inference including point estimation for the parameter, its standard error, and the corresponding \(100(1-\alpha)\%\) credible interval, goodness-of-fit measures, asymptotic \(100(1-\alpha)\%\) confidence interval (CI) and corresponding standard errors, and Fisher information matix.

References

J. A. Achcar 1993. Inferences for the Birnbaum-Saunders fatigue life model using Bayesian methods, Computational Statistics \& Data Analysis, 15 (4), 367-380.

Examples

# NOT RUN {
data(fatigue)
x <- fatigue
Jeffreysbs(x, CI = 0.95, M0 = 800, M = 1000)
# }