By Dr Gregory Clive Taylor

Preprints

Al-Mudafer MT; Avanzi B; Taylor G; Wong B, 2021, Stochastic loss reserving with mixture density neural networks, http://dx.doi.org/10.48550/arxiv.2108.07924

Avanzi B; Taylor G; Wong B; Xian A, 2020, Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework, http://dx.doi.org/10.48550/arxiv.2003.13888

Avanzi B; Taylor G; Vu PA; Wong B, A Multivariate Evolutionary Generalised Linear Model Framework with Adaptive Estimation for Claims Reserving, http://dx.doi.org/10.2139/ssrn.3413016

Avanzi B; Taylor G; Wong B; Yang X, A Multivariate Micro-Level Insurance Counts Model With a Cox Process Approach, http://dx.doi.org/10.2139/ssrn.3354434

Avanzi B; Taylor G, Common Shock Models for Claim Arrays, http://dx.doi.org/10.2139/ssrn.2881058

Avanzi B; Taylor G; Wong B; Xian A, Inference of Counts Using Markov-Modulated Non-Homogeneous Poisson Processes, http://dx.doi.org/10.2139/ssrn.3354342

Avanzi B; Taylor G; Vu PA; Wong B, On Unbalanced Data and Common Shock Models in Stochastic Loss Reserving, http://dx.doi.org/10.2139/ssrn.3303255

Avanzi B; Taylor G; Vu PA, Stochastic Loss Reserving with Dependence: A Flexible Multivariate Tweedie Approach, http://dx.doi.org/10.2139/ssrn.2753540

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