Jeffreysbs {bibs} | R Documentation |
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.
Author(s)
Mahdi Teimouri
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
data(fatigue)
x <- fatigue
Jeffreysbs(x, CI = 0.95, M0 = 800, M = 1000)