408 citations · 515 across the 20 of their papers we have counts for
22 papers
Bayesian Modelling of Nonstationary Extreme Values Using a Nonparametric Hawkes Process
Gordon J. Ross, Dean Markwick
Modelling and forecasting the occurrence of extreme events is especially difficult when the event process is nonstationary, with changes in both the rate at which extremes occur an…
Sequential Bayesian Monitoring for Recoverable and Drifting Processes
Gordon J. Ross
In many Phase II statistical process control (SPC) problems, the main concern is not whether a monitored process has ever changed, but whether it is currently operating at an accep…
The Ancestor Hawkes Process with an Application to Group Chat Data
Gordon J Ross, Isabella Deutsch
The Hawkes process is used to model point process data where events occur in clusters and bursts. In a standard multivariate Hawkes process, every event that occurs in a dimension…
dirichletprocess: An R Package for Fitting Complex Bayesian Nonparametric Models
Gordon J. Ross, Dean Markwick, Priyanshu Tiwari
The dirichletprocess package provides software for creating flexible Dirichlet process objects. Users can perform nonparametric Bayesian analysis using Dirichlet processes without…
A Semiparametric Discrete Hawkes Model with a Collapsed Gaussian-Process Prior
Trinnhallen Brisley, Gordon Ross, Daniel Paulin
Hawkes processes are used in settings where past events increase the likelihood of future events occurring, resulting in a natural clustering structure. Traditional Hawkes process…
Estimation of Multivariate Discrete Hawkes Processes: An Application to Incident Monitoring
Trinnhallen Brisley, Gordon Ross, Daniel Paulin +1
Hawkes processes are a class of self-exciting point processes that are used to model complex phenomena. While most applications of Hawkes processes assume that event data occurs in…
A Point Mass Proposal Method for Bayesian State-Space Model Fitting
Mary Llewellyn, Ruth King, Víctor Elvira +1
State-space models (SSMs) are commonly used to model time series data where the observations depend on an unobserved latent process. However, inference on the model parameters of a…
Estimating Product Cannibalisation in Wholesale using Multivariate Hawkes Processes with Inhibition
Isabella Deutsch, Gordon J. Ross
Product cannibalisation in the marketplace refers to the decrease in the sales of one product due to competition from another product. We examine this phenomenon in a wholesale dat…
Bayesian Estimation of the ETAS Model for Earthquake Occurrences
Gordon J Ross
The Epidemic Type Aftershock Sequence (ETAS) model is one of the most widely-used approaches to seismic forecasting. However most studies of ETAS use point estimates for the model…
Nonparametric Detection of Multiple Location-Scale Change Points via Wild Binary Segmentation
Gordon J. Ross
Change point methods are used to divide a sequence of observations into segments with different behaviour. Often, the distributional form of the observations is unknown, but the ch…
ABC Learning of Hawkes Processes with Missing or Noisy Event Times
Isabella Deutsch, Gordon J. Ross
The self-exciting Hawkes process is widely used to model events which occur in bursts. However, many real world data sets contain missing events and/or noisily observed event times…
Semiparametric Bayesian Forecasting of Spatial Earthquake Occurrences
Aleksandar A. Kolev, Gordon J. Ross
Self-exciting Hawkes processes are used to model events which cluster in time and space, and have been widely studied in seismology under the name of the Epidemic Type Aftershock S…
Bayesian hierarchical modelling of sparse count processes in retail analytics
James Pitkin, Ioanna Manolopoulou, Gordon Ross
The field of retail analytics has been transformed by the availability of rich data which can be used to perform tasks such as demand forecasting and inventory management. However,…
Dirichlet Process Mixtures of Order Statistics with Applications to Retail Analytics
James Pitkin, Gordon Ross, Ioanna Manolopoulou
The rise of "big data" has led to the frequent need to process and store datasets containing large numbers of high dimensional observations. Due to storage restrictions, these obse…
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models
Enrico Mariconti, Lucky Onwuzurike, Panagiotis Andriotis +3
The rise in popularity of the Android platform has resulted in an explosion of malware threats targeting it. As both Android malware and the operating system itself constantly evol…
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Apostolos Pyrgelis, Emiliano De Cristofaro, Gordon Ross
Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained coll…
Dynamic Multi-Factor Clustering of Financial Networks
Gordon J. Ross
We investigate the tendency for financial instruments to form clusters when there are multiple factors influencing the correlation structure. Specifically, we consider a stock port…
Understanding the Heavy Tailed Dynamics in Human Behavior
Gordon J Ross, Tim Jones
The recent availability of electronic datasets containing large volumes of communication data has made it possible to study human behavior on a larger scale than ever before. From…
Sequential Change Detection in the Presence of Unknown Parameters
Gordon J Ross
It is commonly required to detect change points in sequences of random variables. In the most difficult setting of this problem, change detection must be performed sequentially wit…
Sequential Monitoring of a Bernoulli Sequence when the Pre-change Parameter is Unknown
Gordon J. Ross, Dimitris K. Tasoulis, Niall M. Adams
The task of monitoring for a change in the mean of a sequence of Bernoulli random variables has been widely studied. However most existing approaches make at least one of the follo…
Exponentially Weighted Moving Average Charts for Detecting Concept Drift
Gordon J. Ross, Niall M. Adams, Dimitris K. Tasoulis +1
Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phe…
Modeling Financial Volatility in the Presence of Abrupt Changes
Gordon J. Ross
The volatility of financial instruments is rarely constant, and usually varies over time. This creates a phenomenon called volatility clustering, where large price movements on one…