Recently, a new cure rate survival model has been proposed by considering the Conway–Maxwell Poisson distribution as the distribution of the competing cause variable. This model includes some of the well-known cure rate models discussed in the literature as special cases. Cancer clinical...

In this paper, we propose a new cure rate survival model, which extends the model of Rodrigues et al. (2011) by incorporating a structure of dependence between the initiated cells. To create the structure of the correlation between the initiated cells, we use an extension of the generalized...

The Weibull family is widely used to model failure data, or lifetime data, although the classical two-parameter Weibull distribution is limited to positive data and monotone failure rate. The parameters of the Weibull model are commonly obtained by maximum likelihood estimation; however, it is...

In this article, reliability sampling plans are developed for the Weibull distribution when the life test is progressively first-failure censored. We use the maximum likelihood method to obtain the point estimators of the model parameters. We propose an approach to establish reliability sampling...

In this paper, we propose a new method for the estimation of parameters of the three-parameter Weibull distribution. The method is based on a data transformation, which avoids the problem of unbounded likelihood. In the proposed method, under mild conditions, the estimates always exist uniquely...

A Bayesian solution is suggested for the modelling of spatial point patterns with inhomogeneous hard-core radius using Gaussian processes in the regularization. The key observation is that a straightforward use of the finite Gibbs hard-core process likelihood together with a log-Gaussian random...

Statistical inference for the models with intractable normalizing constants has attracted much attention. During the past two decades, various approximation- or simulation-based methods have been proposed for the problem, such as the Monte Carlo maximum likelihood method and the auxiliary...

We develop a Bayesian dynamic model for modeling and forecasting multivariate time series relaxing the assumption of normality for the initial distribution of the state space parameter, and replacing it by a more flexible class of distributions, which we call Generalized Skew-Normal (GSN)...

This paper builds on recent research that focuses on regression modeling of continuous bounded data, such as proportions measured on a continuous scale. Specifically, it deals with beta regression models with mixed effects from a Bayesian approach. We use a suitable parameterization of the beta...

Traffic flow data are primarily used to monitor road use and to compute road accident rates in Great Britain. The main traffic flow data used for these purposes measure annual traffic flow in vehicle kilometres, however this dataset is limited in its disaggregation. In particular, it is not...