Bathtub shape is one of the most important behaviors of the hazard rate function that is quite common in lifetime data analysis. Such shapes are actually the combination of three different shapes and, as such, there have been several proposals to model such behavior. One such proposal is to combine at most three different distributions, often the Weibull or some similar model, separately for de...

The present article considers a new function to propose a new lifetime distribution. The new distribution is introduced by mixing up a linear system of the two logarithms of cumulative hazard functions. The proposed model is called new extended flexible Weibull distribution and is able to model lifetime with bathtub shaped failure rates and offers greater flexibility. Therefore, it can be quite...

Weibull mixtures have been used extensively in reliability and survival analysis, and they have also been generalized by allowing negative mixing weights, which arise naturally under the formation of some structures of reliability systems. These models provide exible distributions for modelling dependent lifetimes from heterogeneous populations. In this paper, we study conditions on the mixing ...

In this paper we will develop further the sequential life test approach presented in a previous article by [1] using an underlying two parameter Inverse Weibull sampling distribution. The location parameter or minimum life will be considered equal to zero. Once again we will provide rules for making one of the three possible decisions as each observation becomes available; that is: accept the n...

To train an inference network jointly with a deep generative topic model, making it both scalable to big corpus and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation (DLDA), which infers posterior samples via a hybrid of stochasticgradient MCMC and autoencoding variational Bayes. The generative network of WHAI has a hi...

The purpose of the present study was to test the hypothesis that glazing will affect the Weibull modulus or the mean flexural strength of dental porcelain. Four groups (n = 30) of specimens were prepared from feldspathic body porcelain (Vita VMK 68). The specimens were tested in groups: original glazed condition (control), and after grinding separately with 1,000-grit, 600-grit, and 100-grit si...

In this paper we consider the Marshall-Olkin bivariate Weibull distribution. The Marshall-Olkin bivariate Weibull distribution is a singular distribution, whose both the marginals are univariate Weibull distributions. This is a generalization of the Marshall-Olkin bivariate exponential distribution. The cumulative joint distribution of the Marshall-Olkin bivariate Weibull distribution is a mixt...

Recently, a new entropy based divergence measure has been introduced which is much like Kullback-Leibler divergence. This entropy measures the distance between an empirical and a prescribed survival function and is a lot easier to compute in continuous distributions than the K-L divergence. In this paper we show that this distance converges to zero with increasing sample size and we apply it to...

By means of Weibull analysis, this study evaluated and compared the flexural strength and shear bond strength of flowable light-cured composites against those of conventional ones. Twenty specimens of each material were prepared for flexural and shear bond strength measurements. Specimens were measured after water storage at 37 degrees C for 24 hours. Three of four flowable composites showed si...

To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and autoencoding variational Bayes. The generative network of WHAI has a hierarc...