-
Peskun-Tierney ordering for Markov chain and process Monte Carlo: beyond the reversible scenario
Historically time-reversibility of the transitions or processes underpin...
read it
-
A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving
In this paper, we develop a multivariate evolutionary generalised linear...
read it
-
The Wreath Process: A totally generative model of geometric shape based on nested symmetries
We consider the problem of modelling noisy but highly symmetric shapes t...
read it
-
Infinite Mixtures of Multivariate Gaussian Processes
This paper presents a new model called infinite mixtures of multivariate...
read it
-
Burglary in London: Insights from Statistical Heterogeneous Spatial Point Processes
To obtain operational insights regarding the crime of burglary in London...
read it
-
Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models
Multivariate count data are defined as the number of items of different ...
read it
-
Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes
We address the problem of prediction and filtering of multivariate data ...
read it
A multivariate micro-level insurance counts model with a Cox process approach
In this paper, we focus on estimating ultimate claim counts in multiple insurance processes and thus extend the associated literature of micro-level stochastic reserving models to the multivariate context. Specifically, we develop a multivariate Cox process to model the joint arrival process of insurance claims in multiple Lines of Business. The dependency structure is introduced via multivariate shot noise intensity processes which are connected with the help of Lévy copulas. Such a construction is more parsimonious and tractable in higher dimensions than plain vanilla common shock models. We also consider practical implementation and explicitly incorporate known covariates, such as seasonality patterns and trends, which may explain some of the relationship between two insurance processes (or at least help tease out those relationships). We develop a filtering algorithm based on the reversible-jump Markov Chain Monte Carlo (RJMCMC) method to estimate the unobservable stochastic intensities. Model calibration is illustrated using real data from the AUSI data set.
READ FULL TEXT
Comments
There are no comments yet.