Hierarchical Transmuted Log-Logistic Model: A Subjective Bayesian Analysis

Santos, Carlos Aparecido dos; Granzotto, Daniele Cristina Tita; Tomazella, Vera; Louzada, Francisco

doi:10.3390/jrfm11010013

Cited by 2 publications

(2 citation statements)

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“…In addition, other authors have studied the Bayesian inference of the LL distribution and some of its generalizations. dos Santos et al [ 22 ] developed a Bayesian analysis of the transmuted LL distribution. Yahaya and Dewu [ 23 ] studied the Bayesian estimation of the scale parameter for the LL distribution using Chi-square and Maxwell priors.…”

Section: Introductionmentioning

confidence: 99%

Bayesian and Classical Inference for the Generalized Log‐Logistic Distribution with Applications to Survival Data

Muse¹,

Mwalili

Ngesa³

et al. 2021

Computational Intelligence and Neuroscience

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The generalized log-logistic distribution is especially useful for modelling survival data with variable hazard rate shapes because it extends the log-logistic distribution by adding an extra parameter to the classical distribution, resulting in greater flexibility in analyzing and modelling various data types. We derive the fundamental mathematical and statistical properties of the proposed distribution in this paper. Many well-known lifetime special submodels are included in the proposed distribution, including the Weibull, log-logistic, exponential, and Burr XII distributions. The maximum likelihood method was used to estimate the unknown parameters of the proposed distribution, and a Monte Carlo simulation study was run to assess the estimators’ performance. This distribution is significant because it can model both monotone and nonmonotone hazard rate functions, which are quite common in survival and reliability data analysis. Furthermore, the proposed distribution’s flexibility and usefulness are demonstrated in a real-world data set and compared to its submodels, the Weibull, log-logistic, and Burr XII distributions, as well as other three-parameter parametric survival distributions, such as the exponentiated Weibull distribution, the three-parameter log-normal distribution, the three-parameter (or the shifted) log-logistic distribution, the three-parameter gamma distribution, and an exponentiated Weibull distribution. The proposed distribution is plausible, according to the goodness-of-fit, log-likelihood, and information criterion values. Finally, for the data set, Bayesian inference and Gibb’s sampling performance are used to compute the approximate Bayes estimates as well as the highest posterior density credible intervals, and the convergence diagnostic techniques based on Markov chain Monte Carlo techniques were used.

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Section: Introductionmentioning

confidence: 99%

Bayesian and Classical Inference for the Generalized Log‐Logistic Distribution with Applications to Survival Data

Muse¹,

Mwalili

Ngesa³

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Dos Santos et al (2018) proposed applying the transmuted log-logistic (TLL) model, which is a generalization of the log-logistic model, in a Bayesian context. The log-logistic model has been used as it is simple and has a unimodal hazard rate, an important characteristic in survival analysis.…”

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confidence: 99%