By Dr Gregory Clive Taylor

Preprints

Avanzi B; Tan X; Taylor G; Wong B, 2024, On the evolution of data breach reporting patterns and frequency in the United States: a cross-state analysis, http://dx.doi.org/10.48550/arxiv.2310.04786

Avanzi B; Lavender M; Taylor G; Wong B, 2023, On the impact of outliers in loss reserving, http://dx.doi.org/10.48550/arxiv.2203.00184

Avanzi B; Lavender M; Taylor G; Wong B, 2023, Detection and treatment of outliers for multivariate robust loss reserving, http://dx.doi.org/10.48550/arxiv.2203.03874

Avanzi B; Taylor G; Wang M; Wong B, 2023, Machine Learning with High-Cardinality Categorical Features in Actuarial Applications, http://dx.doi.org/10.48550/arxiv.2301.12710

Mourdoukoutas F; Pantelous AA; Taylor G, 2022, Competitive Insurance Pricing Strategies for Multiple Lines of Business: A Game Theoretic Approach, http://dx.doi.org/10.2139/ssrn.4049437

Taylor G; Vu PA, 2021, Auto-balanced common shock claim models, http://dx.doi.org/10.48550/arxiv.2112.14715

Avanzi B; Taylor GC; Wang M; Wong B, 2021, SynthETIC: an individual insurance claim simulator with feature control, http://dx.doi.org/10.48550/arxiv.2008.05693

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 GC; Wong B; Yang X, 2020, On the modelling of multivariate counts with Cox processes and dependent shot noise intensities, http://dx.doi.org/10.48550/arxiv.2004.11169

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 GC; Vu PA; Wong B, 2020, On unbalanced data and common shock models in stochastic loss reserving, http://dx.doi.org/10.48550/arxiv.2005.03500

Avanzi B; Taylor GC; Vu PA; Wong B, 2020, A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving, http://dx.doi.org/10.48550/arxiv.2004.06880

Taylor G, 2020, Loss Reserving Prediction Error with Special Reference to a Tweedie Sub-Family, http://dx.doi.org/10.2139/ssrn.3642378

Avanzi B; Taylor G; Wong B; Xian A, 2019, Modelling and Understanding Count Processes Through a Markov-Modulated Non-Homogeneous Poisson Process Framework, http://dx.doi.org/10.2139/ssrn.3354342

McGuire G; Taylor G; Miller H, 2018, Self-Assembling Insurance Claim Models Using Regularized Regression and Machine Learning, http://dx.doi.org/10.2139/ssrn.3241906

Taylor G, 2018, Observations on Industry Practice in the Construction of Large Correlation Structures for Risk and Capital Margins, http://dx.doi.org/10.2139/ssrn.3113756

Avanzi B; Taylor G; Wong B, 2016, Common Shock Models for Claim Arrays, http://dx.doi.org/10.2139/ssrn.2881058

Avanzi B; Taylor G; Vu PA; Wong B, 2016, Stochastic Loss Reserving with Dependence: A Flexible Multivariate Tweedie Approach, http://dx.doi.org/10.2139/ssrn.2753540

Taylor G, 2015, Existence and Uniqueness of Chain Ladder Solutions, http://dx.doi.org/10.2139/ssrn.2660053

Taylor G, A Cape Cod Model for the Exponential Dispersion Family, http://dx.doi.org/10.2139/ssrn.3201982

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

Taylor G, An Iterated Bornhuetter-Ferguson Model, http://dx.doi.org/10.2139/ssrn.3225556

Taylor G, Claim Models: Granular and Machine Learning Forms, http://dx.doi.org/10.2139/ssrn.3387702

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

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