Discovery of statistical equivalence classes using computer algebra

Discrete statistical models supported on labelled event trees can be specified using so-called interpolating polynomials which are generalizations of generating functions. These admit a nested representation. A new algorithm exploits the primary decomposition of monomial ideals associated with an interpolating polynomial to quickly compute all nested representations of that polynomial. It hereby determines an important subclass of all trees representing the same statistical model. To illustrate this method we analyze the full polynomial equivalence class of a staged tree representing the best fitting model inferred from a real-world dataset.Comment: 26 pages, 9 figure

Development of Bayesian analysis program for extraction of polarisation observables at CLAS

At the mass scale of a proton, the strong force is not well understood. Various quark models exist, but it is important to determine which quark model(s) are most accurate. Experimentally, finding resonances predicted by some models and not others would give valuable insight into this fundamental interaction. Several labs around the world use photoproduction experiments to find these missing resonances. The aim of this work is to develop a robust Bayesian data analysis program for extracting polarisation observables from pseudoscalar meson photoproduction experiments using CLAS at Jefferson Lab. This method, known as nested sampling, has been compared to traditional methods and has incorporated data parallelisation and GPU programming. It involves an event-by-event likelihood function, which has no associated loss of information from histogram binning, and results can be easily constrained to the physical region. One of the most important advantages of the nested sampling approach is that data from different experiments can be combined and analysed simultaneously. Results on both simulated and previously analysed experimental data for the K+Λ channel will be discussed

Nested Event Extraction upon Pivot Element Recogniton

Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of outer events and as triggers of inner events, and thus connect them into nested structures. This special characteristic of PEs brings challenges to existing NEE methods, as they cannot well cope with the dual identities of PEs. Therefore, this paper proposes a new model, called PerNee, which extracts nested events mainly based on recognizing PEs. Specifically, PerNee first recognizes the triggers of both inner and outer events and further recognizes the PEs via classifying the relation type between trigger pairs. In order to obtain better representations of triggers and arguments to further improve NEE performance, it incorporates the information of both event types and argument roles into PerNee through prompt learning. Since existing NEE datasets (e.g., Genia11) are limited to specific domains and contain a narrow range of event types with nested structures, we systematically categorize nested events in generic domain and construct a new NEE dataset, namely ACE2005-Nest. Experimental results demonstrate that PerNee consistently achieves state-of-the-art performance on ACE2005-Nest, Genia11 and Genia13

The Careers of Top Managers and Firm Openness: Internal Versus External Labour Markets

This paper studies the careers of top managers using a large panel of firms. The main objective is to empirically evaluate the role of learning and human capital acquisition in promotion dynamics along with variables capturing the formation of internal labour market (ILM) practices. We find that promotion is negatively correlated with tenure, but that there is a non-linear negative duration dependence with elapsed time since the last promotion event. Firms showing a weaker degree of ILM are less prone to promote insiders. We next take the manager's career inside a firm as a sequence of promotion decisions, and use a nested structure of the promotion decision modelled as a nested logit model. �Results show that the top manager's progression nest into four types: loser, early starter, late beginner, and champion, and that the degree of ILM as a signigicant impact on the process of learning inside the firm.

An 11-year validation of wave-surge modelling in the Irish Sea, using a nested POLCOMS-WAM modelling system

In the future it is believed that extreme coastal flooding events will increase (in frequency and intensity) as a result of climate change. We are investigating the flood risks in the eastern Irish Sea posed by extreme storm events. Here, an 11-year simulation (01/01/1996–01/01/2007) including wave–current interaction has been validated. These data can then be used to investigate the potential for coastal flooding in the study area. To accurately model a storm event in the eastern Irish Sea both wave effects and the influence of the external surge need to be considered. To simulate the waves, we have set up a one-way nested approach from a 1° North Atlantic model, to a 1.85 km Irish Sea model, using the state-of-the-art 3rd-generation spectral WAve Model (WAM). This allows the influence of swell to be correctly represented. The Proudman Oceanographic Laboratory Coastal-Ocean Modelling System (POLCOMS) has been used to model the tide–surge interaction. To include the external surge we have set up a one-way nested approach from the 1/9° by 1/6° operational Continental Shelf surge model, to a 1.85 km Irish Sea model. For the high resolution Irish Sea model we use a POLCOMS–WAM coupled model, to allow for the effects of wave–current interaction on the prediction of surges at the coast. Using two classification schemes the coupled model is shown to be good and often very good at predicting the surge, total water elevation and wave conditions. We also find the number of low level surge events has increased in the study area over the past decade. However, this time period is too short to determine any long-term trends in the wave and surge levels

A comparison of three prediction modelling approaches for clustered survival data with application to Lynch Syndrome Family

The purpose of this study was to compare shared frailty model, joint frailty model and joint nested frailty model in terms of model fitting and prediction accuracy, as applied to Lynch Syndrome family data. The specific question we wanted to address was how the intervals between screening visits affect the risk of developing colorectal cancer among Lynch Syndrome family members. We also addressed questions on how the screening process has an effect on mortality and risks of developing different stages of colorectal cancer. Results from the models show that joint nested frailty model is preferable. This model improves the prediction accuracy by jointly modeling recurrent screenings and terminal event at the same time accounts for both individual and familial correlation