Usually, the parameters of a Weibull distribution are estimated by maximum likelihood estimation. To reduce the biases of the maximum likelihood estimators (MLEs) of two-parameter Weibull distributions, we propose analytic bias-corrected MLEs. Two other common estimators of Weibull distributions, least-squares estimators and percentiles estimators, are also introduced. Based on a comparison of ...

The extreme value theory is widely used in economic and environmental domains, it aims to study the stochastic behaviors associated with rare events. In this context, we consider a new mixture model for extremal events analysis, including Dirichlet process of Weibull (DPMW) distribution below threshold point (PP) upper tail. This developed regression structure PP parameters, which explains vari...

In the analysis of survival data with parametric models, it is well known that the Weibull model is not suitable for modeling survival data where the hazard rate is non-monotonic. For such cases, where hazard rates are bathtub-shaped or unimodal (or hump-shaped), log-logistic, lognormal, Birnbaun-Saunders, and inverse Gaussian models are used for the computational simplicity and popularity amon...

The cause-specific hazard function plays an important role in developing the regression models for competing risks survival data. Proportional hazards and additive are commonly used approaches analysis. Mostly, literature, proportional model was parametric modelling of In this article, we introduce a analysis with risks. For employing consider modified Weibull distribution as baseline which is ...

Abstract The dynamics of the wild boar population has become a pressing issue not only for ecological purposes, but also agricultural and livestock production. data related to dispersal distance can have complex structure, including excess zeros right-censored observations, thus being challenging modeling. In this sense, we propose two different zero-inflated-right-censored regression models, a...

Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically occurs in count datasets. It is highly desirable to have a unified model that can automatically adapt to the underlying dispersion and that can b...

This paper undertakes a reliability study using a Bayesian survival analysis framework to explore the impact of a locomotive wheel’s installed position on its service lifetime and to predict its reliability characteristics. The Bayesian Exponential Regression Model, Bayesian Weibull Regression Model and Bayesian Log-normal Regression Model are used to analyze the lifetime of locomotive wheels u...

Semi-parametric Cox regression and parametric methods have been used to analyze survival data of cancer; however, no study has focused on the comparison of survival models in genetic association analysis of age at onset (AAO) of cancer. The Hepatocyte nuclear factor-1beta (HNF1B) gene has been associated with risk of endometrial and prostate cancers; however, no study has focused on the effect ...

In wood engineering, lower quantile estimation is vital to the safety of the construction with wood materials. In this thesis, we will first study the censored Weibull maximum likelihood estimate (MLE) of the lower quantile as in the current industrial standard D5457 (ASTM, 2004a) from a statistical point of view. According to our simulations, the lower quantile estimated by the censored Weibul...