Journal ArticleDOI
A Comparison of Maximum Likelihood and Bayesian Estimators for the Three-Parameter Weibull Distribution
Richard Smith,J. C. Naylor +1 more
TLDR
In this article, maximum likelihood and Bayesian estimators are developed and compared for the three-parameter Weibull distribution, and the authors conclude that there are practical advantages to the Bayesian approach.Abstract:
Maximum likelihood and Bayesian estimators are developed and compared for the three‐parameter Weibull distribution. For the data analysed in the paper, the two sets of estimators are found to be very different. The reasons for this are explored, and ways of reducing the discrepancy, including reparametrization, are investigated. Our overall conclusion is that there are practical advantages to the Bayesian approach.read more
Citations
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Journal ArticleDOI
Marshall Olkin Alpha Power Extended Weibull Distribution: Different Methods of Estimation based on Type I and Type II Censoring
Journal Article
The Generalized Odd Lomax Generated Family of Distributions with Applications
TL;DR: In this paper, a new generated family of distributions under the name of "The generalized odd Lomax-G family" by adding three additional parameters to generalize any continuous baseline distribution is provided.
Journal ArticleDOI
Bimodal extension based on the skew-$t$-normal distribution
TL;DR: In this paper, a skew and uni/bi-modal extension of the Student-$t$ distribution is considered, which has wider ranges of skewness and kurtosis than the other skew distributions in literature.
References
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Brian Francis,Jerald F. Lawless +1 more
Journal ArticleDOI
Maximum likelihood estimation in a class of nonregular cases
TL;DR: In this article, the authors consider maximum likelihood estimation of the parameters of a probability density which is zero for x 2, the information matrix is finite and the classical asymptotic properties continue to hold.
Journal ArticleDOI
Estimating Parameters in Continuous Univariate Distributions with a Shifted Origin
TL;DR: In this paper, a general method of estimating parameters in continuous univariate distributions is proposed, which is especially suited to cases where one of the parameters is an unknown shifted origin and is shown to give consistent estimators with asymptotic efficiency equal to ML estimators when these exist.
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