Description Usage Arguments Value Author(s) References Examples
Estimates parameters of the Birnbaum-Saunders family in a Bayesian framework through the Metropolis-Hasting algorithm when subjects are placed on progressive type-II censoring scheme with likelihood function
l(α,β|x_{1:m:n},…,x_{m:m:n})=\log L(Θ) \propto C ∑_{i=1}^{m} \log f(x_{i:m:n}{{;}}|α,β) + ∑_{i=1}^{m} R_i \log \bigl[1-F(x_{i:m:n}{{;}}|α,β)\bigr],
in which F(.|α,β) is cumulative distribution function of the Birnbaum-Saunders family with C=n(n-R_1-1)(n-R_1-R_2-2)… (n-R_1-R_2-…-R_{m-1}-m+1). The acceptance for each new sample of α and β, respectively, becomes
A_{α}=\min ≤ft\{1,∏_{i=1}^{m}\frac{\bigl[1-F_{BS}(t_{i:m:n}|1/(α^{new})^2,β)\bigr]^{R_{i}}}{\bigl[1-F_{BS}(t_{i:m:n}|1/(α_{old})^2,β)\bigr]^{R_{i}}}\right\}
,
A_{β}=\min ≤ft\{1,∏_{i=1}^{m}\frac{\bigl[1-F_{BS}(t_{i:m:n}|α,β^{new})\bigr]^{R_{i}}}{\bigl[1-F_{BS}(t_{i:m:n}|α,β_{old})\bigr]^{R_{i}}}\right\}.
1 | typeIIbs(plan, M0 = 4000, M = 6000, CI = 0.95)
|
plan |
Censoring plan for progressive type-II censoring scheme. It must be given as a |
M0 |
The number of sampler runs considered as burn-in. |
M |
The number of total sampler runs. |
CI |
Confidence or coverage level for constructing percentile confidence interval. That is 0.95 by default. |
A list including summary statistics after burn-in point including: mean, median, standard deviation, 100(1 - CI
)/2 percentile, 100(1/2 + CI
/2) percentile.
Mahdi Teimouri
M. Teimouri and S. Nadarajah 2016. Bias corrected MLEs under progressive type-II censoring scheme, Journal of Statistical Computation and Simulation, 86 (14), 2714-2726.
N. Balakrishnan and R. Aggarwala 2000. Progressive Censoring: Theory, Methods, and Applications. Springer Science \& Business Media, New York.
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