In this we develops a ridge estimator for the Zero-Inflated Probit Bell (ZIPBell) regression model. The ZIPBell model adapts the Zero-Inflated Bell (ZIBell) model originally proposed by Lemonte et al. (2019) by employing a probit link function for the zero-inflation component. Our contribution lies in incorporating ridge penalization into this framework, providing a methodology that stabilizes parameter estimates by reducing variance and mitigating multicollinearity effects without excluding correlated predictors.

Résumé

We introduce a new family of distributions based on Topp–Leone Kumaraswamy, designed for data modeling. We analyze its mathematical properties, moments, and stochastic characteristics, proposing a parameter estimation method using maximum likelihood. The model’s applicability is demonstrated through data on development indicators in Benin, compared with competing models. The promising results highlight the relevance of this new family for statistical analysis and decision-making in socio- economic development.

Bayesian estimation of the tail index of a heavy-tailed distribution is addressed when data are randomly right-censored. Maximum a posteriori and mean posterior estimators are constructed for various prior distributions of the tail index. Their finite-sample properties are investigated via simulations. Tail index estimation requires selecting an appropriate threshold for constructing relative excesses. A Monte Carlo procedure is proposed for tackling this issue. Finally, the proposed estimators are illustrated on a medical dataset.

We consider in this paper, a general class of stochastic differential equations driven by stable processes with Lipschitz drift  coefficients and non-Lipschitz diffusion coefficients. A strong Euler-Maruyama approximate  solution  is proved whenever the diffusion coefficient is  Hölder continuous with exponent satisfying some condition.  We derive also the  strong rate of convergence.

In this talk, we consider a linear birth and death process with immigration to model infectious disease propagation when contamination stems from both person-to-person contact and the environment. Our aim is to estimate the parameters

Time homogeneous Markov model has been successfully used to extend the
clas sical survival analysis to the multi-states analysis. This model assumes
that the evolution of the process is independent to the waiting time in the state.
In our clinical problem, this constraint is restrictive. The semi-Markov can be
used to extend the time-homogeneous Markov model with discrete states and