The mortality dynamics experienced in the latest decades, especially at adult and old ages, has motivated the introduction of major innovations in the modeling of mortality for actuarial applications; such innovations concern, in particular, the representation of the uncertainty relating to...

We study a canonical setting of learning in networks where initially agents receive conditionally i.i.d. signals about a binary state. The distribution according to which signals are drawn is called an information structure. Agents repeatedly communicate beliefs with their neighbors and update...

In this paper, we model dependence between operational risks by allowing risk profiles to evolve stochastically in time and to be dependent. This allows for a flexible correlation structure where the dependence between frequencies of different risk categories and between severities of different...

This paper considers sampling in proportion to size from a partly unknown distribution. The applied context is the exploration for undiscovered resources, like oil accumulations in different deposits, where the most promising deposits are likely to be drilled first, based on some geologic size...

In this paper, a very general model of survival data with (exclusive or inclusive) right censoring, explanatory processes and a baseline predictable hazard function is considered in the ocntezt of nonparametric Bayasian analysis. particular cases are semiparametric proportional hazards and...

A crucial role belongs to export firms in the export-led growth model of Vietnam. The COVID-19 disease has posed a serious challenge to the Vietnamese economy, having negatively impacted its influential export sector. However, investigating this export sector encounters small sample issues...

This article is the second of a series discussing the Bayesian Network representation of multivariate normal distributions. In the first article we introduced a cascading regressions model leading to a Bayesian network representation of any joint normal distribution [Pap22]. A joint normal...

This article is the first of a series discussing the Bayesian Network representation of multivariate normal distributions. A joint normal distribution being fully specified by its mean vector and its covariance matrix is not simple to interact with as its Bayesian network equivalent....

Despite there are useful books and text books from recognized authors about modeling macroeconomics through various types of methods and methodologies, “Some Useful tips in Modeling a DSGE models” try to add special features through an economist can use to model macro and micro relations to...

A general model is proposed for flexibly estimating the density of a continuous response variable conditional on a possibly high-dimensional set of covariates. The model is a finite mixture of asymmetric student-t densities with covariate dependent mixture weights. The four parameters of the...