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.
Organisation : Jean-François Dupuy (INSA de Rennes), Ouagnina Hili (Institut National Polytechnique Félix Houphouët-Boigny), Solym Manou-Abi (LMA-Université de Poitiers),
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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