Search Results for author: Bernard Wong

Found 8 papers, 3 papers with code

Machine Learning with High-Cardinality Categorical Features in Actuarial Applications

1 code implementation30 Jan 2023 Benjamin Avanzi, Greg Taylor, Melantha Wang, Bernard Wong

The GLMMNet integrates a generalised linear mixed model in a deep learning framework, offering the predictive power of neural networks and the transparency of random effects estimates, the latter of which cannot be obtained from the entity embedding models.

Vocal Bursts Intensity Prediction

SynthETIC: an individual insurance claim simulator with feature control

2 code implementations13 Aug 2020 Benjamin Avanzi, Gregory Clive Taylor, Melantha Wang, Bernard Wong

In short, they are likely to be useful in the analysis of any data set whose volume is sufficient to obscure a naked-eye view of its features.

On the optimality of joint periodic and extraordinary dividend strategies

no code implementations1 Jun 2020 Benjamin Avanzi, Hayden Lau, Bernard Wong

We determine which strategies (either periodic, immediate, or hybrid) are optimal, that is, we show which are the strategies that maximise the expected present value of dividends paid until ruin, net of transaction costs.

On unbalanced data and common shock models in stochastic loss reserving

no code implementations7 May 2020 Benjamin Avanzi, Gregory Clive Taylor, Phuong Anh Vu, Bernard Wong

In this paper, we address this problem in the loss reserving context using a common shock Tweedie approach for unbalanced data.

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

no code implementations23 Apr 2020 Benjamin Avanzi, Gregory Clive Taylor, Bernard Wong, Xinda Yang

In this paper, we develop a method to model and estimate several, _dependent_ count processes, using granular data.

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

no code implementations31 Mar 2020 Benjamin Avanzi, Greg Taylor, Bernard Wong, Alan Xian

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

Fault Tolerance for Service Function Chains

1 code implementation10 Jan 2020 Milad Ghaznavi, Elaheh Jalalpour, Bernard Wong, Raouf Boutaba, Ali Jose Mashtizadeh

Enterprise network traffic typically traverses a sequence of middleboxes forming a service function chain, or simply a chain.

Networking and Internet Architecture Systems and Control Systems and Control

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