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G. Taylor

10 papers hereh-index 221.8k citations123 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author1
  • middle author8

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • q-fin.RM4
  • stat.ME4
  • stat.AP1
  • stat.ML1
same name
  • G. Taylor — 123 papers, h 0
  • G. Taylor — 101 papers, h 48
  • G. Taylor — 36 papers, h 35
  • G. Taylor — 33 papers, h 22
  • G. Taylor — 32 papers, h 79
  • G. Taylor — 27 papers, h 17

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202023
most citedModelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework

22 citations · 82 across the 10 of their papers we have counts for

collaborators
Showing 2020Show all

3 papers · 1 filter

q-fin.RM2020★ 14 cited

SynthETIC: an individual insurance claim simulator with feature control

Benjamin Avanzi, Gregory Clive Taylor, Melantha Wang +1

Recent years have seen rapid increase in the application of machine learning to insurance loss reserving. They yield most value when applied to large data sets, such as individual…

q-fin.RM2020★ 5 cited

On the modelling of multivariate counts with Cox processes and dependent shot noise intensities

Benjamin Avanzi, Gregory Clive Taylor, Bernard Wong +1

In this paper, we develop a method to model and estimate several, _dependent_ count processes, using granular data. Specifically, we develop a multivariate Cox process with shot no…

q-fin.RM2020★ 22 cited

Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework

Benjamin Avanzi, Greg Taylor, Bernard Wong +1

The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we e…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.