The heteroscedastic odd log-logistic generalized gamma regression model for censored data

Prataviera, Fábio; Ortega, Edwin M. M.; Cordeiro, Gauss M.; Braga, Altemir da Silva

doi:10.1080/03610918.2018.1423695

Cited by 12 publications

(8 citation statements)

References 2 publications

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“…Plots of (4.1) in Figure 5 show the bimodality and great flexibility of the density of Y . Recently, some extended regressions can be found in [19,[21][22][23]. In a similar manner, we propose the LMOOLLW regression as an alternative for modeling four types of failure rate functions.…”

Section: The Lmoollw Regressionmentioning

confidence: 97%

A new extended log-Weibull regression: Simulations and applications

Cordeiro

Tahir

Vasconcelos

et al. 2021

Hacettepe Journal of Mathematics and Statistics

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The induction of one or more parameter(s) in parent distributions opened new doors for flexible modeling in modern distribution theory. Among well-established generalized (G) classes for flexible modeling, the exponentiated-G, Marshall-Olkin-G and odd log-logistic-G families offer induction of one additional parameter while the beta-G and Kumaraswamy-G classes offer two extra shape parameters. The Marshall-Olkin-odd-loglogistic-G (MOOLL-G) family serves as an alternative to the beta-G and Kumaraswamy-G classes. A new motivation for the MOOLL-G family for competing risk scenarios, some useful properties, and parameter estimation are addressed. The new log-MOOLL-Weibull regression is useful for analysis of real life data. The accuracy of the estimates and the residuals is addressed via Monte Carlo simulations. The presented models outperform some other well-known models.

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Section: The Lmoollw Regressionmentioning

confidence: 97%

A new extended log-Weibull regression: Simulations and applications

Cordeiro

Tahir

Vasconcelos

et al. 2021

Hacettepe Journal of Mathematics and Statistics

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“…These include log-Burr XII regression models by [1], a log-extended Weibull regression model by [2], the log-Burr XII regression model for grouped survival data by [3], the log-generalized modified Weibull regression model by [4], the Marshall-Olkin truncated Poisson Weibull regression model by [5], an extended Weibull regression model by [6], the log-beta Weibull regression model by [7,8] proposed two new regressions, one is parametric and the other is partially linear based on an extended Birnbaum-Saunders distribution, and [9] defined a class of survival models for modeling time-to-event with long-term and obtained some of its structural properties. Also, [10] introduced and study the log-odd log-logistic Weibull (LOLLW) distribution and constructed the LOLLW regression model to investigate the informative censoring mechanism in a type of location-scale regression model, [11] introduced the generalized odd log-logistic flexible Weibull regression model, [12] proposed the heteroscedastic log-odd log-logistic generalized gamma (LOLLGG) regression model for censored data, [13] introduced the logit exponentiated power exponential regression model, [14] proposed the generalized odd log-logistic Maxwell semiparametric regression model which is very flexible for modeling response variable with positive support, [15] introduced a new regression based on the odd log-logistic Marshall-Olkin normal distribution, and [16] proposed two different zero-inflated-rightcensored regression models, assuming Weibull and gamma distributions.…”

Section: Introductionmentioning

confidence: 99%

Influence Diagnostic in Log-Exponential-Inverse- Exponential {Weibull} Regression Failure Model

Ahmad¹

2023

AJPAS

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An exponential-inverse-exponential {Weibull} regression failure model is introduced. Some of its properties like density function, survival function, and hazard function are derived. Maximum likelihood estimates of the parameters of the new model from censored data are obtained. To assess the local influence diagnostic(s) on the parameter estimates, the appropriate matrices are derived. Also, global influence and local influence are used to detect influential observations. Martingale and Deviance residuals are obtained and used to detect outliers and evaluate the model assumptions. A real data is analyzed under Log-Exponential-Inverse-Exponential {Weibull} regression model to show the usefulness of the model. A simulation study is performed to investigate the behavior of the estimates for different sample sizes and censoring percentages.

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“…Bourguignon et al [8] proposed the truncated Stacy and showed that its hazard rate function (hrf) has five different shapes. Prataviera et al [9] defined the odd log-logistic-Stacy (OLLS) distribution, which allows bathtub-shaped and unimodal hrf, and proposed a heteroscedastic log-OLLS regression to censored data. Ortega et al [10] derived diagnostic measures to the Stacy regression, and [11] addressed this regression for long-term survivors.…”

Section: Introductionmentioning

confidence: 99%

The Stacy-G Class: A New Family of Distributions with Regression Modeling and Applications to Survival Real Data

Ribeiro-Reis

Cordeiro

Lima

2022

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We study the Stacy-G family, which extends the gamma-G class and provides four of the most well-known forms of the hazard rate function: increasing, decreasing, bathtub, and inverted bathtub. We provide some of its structural properties. We estimate the parameters by maximum likelihood, and perform a simulation study to verify the asymptotic properties of the estimators for the Burr-XII baseline. We construct the log-Stacy-Burr XII regression for censored data. The usefulness of the new models is shown through applications to uncensored and censored real data.

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The heteroscedastic odd log-logistic generalized gamma regression model for censored data

Cited by 12 publications

References 2 publications

A new extended log-Weibull regression: Simulations and applications

A new extended log-Weibull regression: Simulations and applications

Influence Diagnostic in Log-Exponential-Inverse- Exponential {Weibull} Regression Failure Model

The Stacy-G Class: A New Family of Distributions with Regression Modeling and Applications to Survival Real Data

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