Name
Abstract | ||
|---|---|---|
The study proposes the exponentiated Perks distribution as a generalization of Perks distribution. This generalized distribution provides monotone nondecreasing and bathtub shaped hazard rate function. We study its mathematical properties including mode, median, quantile function and order statistics. The estimation of the model parameters is discussed both in classical and Bayesian setups. The maximum likelihood estimates along with their standard errors and confidence intervals have been obtained. For Bayesian estimation, we use independent gamma priors for the model parameters. The posterior densities of the parameters are simulated using Metropolis–Hastings algorithm to obtain sample-based estimates and highest posterior density intervals. Applications of the proposed distribution to three real data sets have been demonstrated. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s13198-016-0451-1 | Int. J. Systems Assurance Engineering and Management |
Keywords | Field | DocType |
Exponentiated Perks distribution, Maximum likelihood estimation, Bayesian estimation, Markov Chain Monte Carlo, Metropolis–Hastings algorithm, Highest posterior density intervals | Mathematical optimization,Metropolis–Hastings algorithm,Bayesian linear regression,Quantile function,Posterior predictive distribution,Maximum a posteriori estimation,Prior probability,Statistics,Order statistic,Bayes estimator,Mathematics | Journal |
Volume | Issue | ISSN |
8 | 2 | 0976-4348 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bhupendra Singh | 1 | 35 | 5.44 |
| Neha Choudhary | 2 | 0 | 1.35 |