Sebastian Ebert (Tilburg) - Measuring Multivariate Risk Preferences

We measure risk preferences for decisions that involve more than a single, monetary attribute. According to theory, correlation aversion, cross- prudence and cross-temperance determine how risk preferences over two single attributes co-vary and interact. We obtain model-free measurements of these cross-risk attitudes in three economic domains, viz., time preferences, social preferences, and preferences over waiting time. This first systematic empirical exploration of multivariate risk preferences provides evidence for assumptions made in economic models on inequality, labor, time preferences, saving, and insurance. We observe non-neutrality of cross-risk attitudes in all domains which questions the de- scriptive accuracy of economic models that assume that utility is additively separable in its arguments.

See: https://sites.google.com/site/ebertecon/home

Ewan Cameron (Oxford, Dept of Zoology)

Progress and (Statistical) Challenges in Malariology

Abstract: In this talk I will describe some key statistical challenges faced by researchers aiming to quantify the burden of disease arising from Plasmodium falciparum malaria at the population level. These include covariate selection in the 'big data' setting, handling spatially-correlated residuals at scale, calibration of individual simulation models of disease transmission, and the embedding of continuous-time, discrete-state Markov Chain solutions within hierarchical Bayesian models. In each case I will describe the pragmatic solutions we've implemented to-date within the Malaria Atlas Project, and highlight more sophisticated solutions we'd like to have in the near-future if the right statistical methodology and computational tools can be identified and/or developed to this end.

References:

http://www.nature.com/nature/journal/v526/n7572/abs/nature15535.html

http://www.nature.com/ncomms/2015/150907/ncomms9170/full/ncomms9170.html

http://www.ncbi.nlm.nih.gov/pubmed/25890035

http://link.springer.com/article/10.1186/s12936-015-0984-9

Presented by: Professor Bernard Silverman FRS (Chief Scientific Adviser to the Home Office)

How many victims of Modern Slavery are there? How long should it be legal to retain a DNA profile on someone who is arrested but not charged? How can we ensure you don’t wait too long to be checked when you enter the country? These and many other questions are the sort of thing where mathematics and statistics play a key role in Home Office policy and operations. I will explain both the role of a departmental Chief Scientific Adviser and also the wider work carried out by the Home Office Science organisation under my leadership. As well as describing some particular problems, such as those set out above, I will also reflect more widely on the way that science and evidence contribute to Government.

Free attendance

There will be a reception after the lecture

Main contact point: paula.matthews@warwick.ac.uk

Downloads: 2016-002-10-bernard-silverman.pdf

Sebastian Ebert (Tilburg) - Measuring Multivariate Risk Preferences

We measure risk preferences for decisions that involve more than a single, monetary attribute. According to theory, correlation aversion, cross- prudence and cross-temperance determine how risk preferences over two single attributes co-vary and interact. We obtain model-free measurements of these cross-risk attitudes in three economic domains, viz., time preferences, social preferences, and preferences over waiting time. This first systematic empirical exploration of multivariate risk preferences provides evidence for assumptions made in economic models on inequality, labor, time preferences, saving, and insurance. We observe non-neutrality of cross-risk attitudes in all domains which questions the de- scriptive accuracy of economic models that assume that utility is additively separable in its arguments.

See: https://sites.google.com/site/ebertecon/home

David Rossell (University of Warwick)

The model separation principle for Bayesian model choice

Abstract: Given a collection of candidate probability models for an observed data y, a fundamental statistical task is to evaluate which models are more likely to have generated y. Tackling this problem within a Bayesian framework requires one to complement the probability model for y (likelihood) with a prior probability model on the parameters (which could be infinitely-dimensional) describing each of the candidate models, as well as to specify model prior probabilities and possibly a utility function. The model separation principle states that the models under consideration should be minimally different from each other, else it becomes hard for us to distinguish them on the bases of the observed y. In the common setting where some of the models are nested this principle is violated, as say Model 1 is a particular case of Model 2 and thus these models are not well separated. We shall review a class of prior distributions called non-local priors (NLPs) as a way to enforce the model separation principle and some of the NLP properties, focusing on parsimony and accelerated convergence rates in high-dimensional inference. We shall illustrate their use in ongoing work related to regression, robust regression and mixture models.
 

Darren Wilkinson (Newcastle University)

Bayesian inference for partially observed Markov processes

Abstract: A number of interesting statistical applications require the estimation of parameters underlying a nonlinear multivariate continuous time Markov process model, using partial and noisy discrete time observations of the system state. Bayesian inference for this problem is difficult due to the fact that the discrete time transition density of the Markov process is typically intractable and computationally intensive to approximate. Nevertheless, it is possible to develop particle MCMC algorithms which are exact, provided that one can simulate exact realisations of the process forwards in time. Such algorithms, often termed "likelihood free" or "plug-and-play" are very attractive, as they allow separation of the problem of model development and simulation implementation from the development of inferential algorithms. Such techniques break down in the case of perfect observation or high-dimensional data, but more efficient algorithms can be developed if one is prepared to deviate from the likelihood free paradigm, at least in the case of diffusion processes. The methods will be illustrated using examples from population dynamics and stochastic biochemical network dynamics.

14.00 - 15.00: David Rossell, “The model separation principle for Bayesian model choice.”
15.00 - 15.30: Coffee break
15.30 - 16.30: Darren Wilkinson, “Bayesian inference for partially observed Markov processes.”

Sebastian Ebert (Tilburg) - Measuring Multivariate Risk Preferences

We measure risk preferences for decisions that involve more than a single, monetary attribute. According to theory, correlation aversion, cross- prudence and cross-temperance determine how risk preferences over two single attributes co-vary and interact. We obtain model-free measurements of these cross-risk attitudes in three economic domains, viz., time preferences, social preferences, and preferences over waiting time. This first systematic empirical exploration of multivariate risk preferences provides evidence for assumptions made in economic models on inequality, labor, time preferences, saving, and insurance. We observe non-neutrality of cross-risk attitudes in all domains which questions the de- scriptive accuracy of economic models that assume that utility is additively separable in its arguments.

See: https://sites.google.com/site/ebertecon/home

Theresa Smith (CHICAS, Lancaster Medical School)

Modelling geo-located health data using spatio-temporal log-Gaussian Cox processes

Abstract: Health data with high spatial and temporal resolution are becoming more common, but there are several practical and computational challenges to using such data to study the relationships between disease risk and possible predictors. These difficulties include lack of measurements on individual-level covariates/exposures, integrating data measured on difference spatial and temporal units, and computational complexity.

In this talk, I outline strategies for jointly estimating systematic (i.e., parametric) trends in disease risk and assessing residual risk with spatio-temporal log-Gaussian Cox processes (LGCPs). In particular, I will present a Bayesian methods and MCMC tools for using spatio-temporal LGCPs to investigate the roles of environmental and socio-economic risk-factors in the incidence of Campylobacter in England.

Hossein Moghimi (Birmingham)

Adaptive Virtual Environments - A Psychophysiological Feedback HCI System Concept
This project aims to design an adaptive dynamic virtual environment, capable of responding to human emotions. Based on the development of a Valence-Arousal-Dominance “Circumplex” (model of emotions), a controllable affective virtual medium (a computer game capable of evoking multiple emotions on the users) has been constructed. The project included five phases: 1) Designing a generic game scenario which can incorporate a large set of variables, with potential variable impact on the users’ emotional experience; 2) Using an online survey with 35 participants to assess the potential emotional impact of each variable; 3) Designing games with combined variables to maximise their emotional effect. The results were validated using additional 68 participants, who played and emotionally rated their experiences. 4) A physiologically-based experiment has been executed, in which the EEG, GSR and Heart Rate of 30 male and female gamers have been recorded during exposure to the most powerful affective environments, identified in the earlier study. A physiological database, with corresponding processed game events and self-reported emotional experiences, has been constructed to be used in the design and evaluation of an affective computing system. 5) More than 700 physiological features have been extracted from the training database, while only minority of them have been selected, to be used in the classification process, using a variable selection algorithm. The selected features have been used to train an affective computing system, using 3 different classification techniques; K-Nearest Neighbour (KNN), Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). The performance of the different classification techniques (each with various settings) have been compared, using a 10-Fold Cross validation. The best classifier has been able to identify subjects’ emotions, with 92% accuracy, using KNN technique while employing only 4 features.