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

Li Su with Michael J. Daniels (MRC Biostatistics Unit)
Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function
Abstract: In long-term follow-up studies, irregular longitudinal data are observed when individuals are assessed repeatedly over time but at uncommon and irregularly spaced time points. Modeling the covariance structure for this type of data is challenging, as it requires specification of a covariance function that is positive definite. Moreover, in certain settings, careful modeling of the covariance structure for irregular longitudinal data can be crucial in order to ensure no bias arises in the mean structure. Two common settings where this occurs are studies with ‘outcome-dependent follow-up’ and studies with ‘ignorable missing data’. ‘Outcome-dependent follow-up’ occurs when individuals with a history of poor health outcomes had more follow-up measurements, and the intervals between the repeated measurements were shorter. When the follow-up time process only depends on previous outcomes, likelihood-based methods can still provide consistent estimates of the regression parameters, given that both the mean and covariance structures of the irregular longitudinal data are correctly specified and no model for the follow-up time process is required. For ‘ignorable missing data’, the missing data mechanism does not need to be specified, but valid likelihood-based inference requires correct specification of the covariance structure. In both cases, flexible modeling approaches for the covariance structure are essential. In this work*, we develop a flexible approach to modeling the covariance structure for irregular continuous longitudinal data using the partial autocorrelation function and the variance function. In particular, we propose semiparametric non-stationary partial autocorrelation function models, which do not suffer from complex positive definiteness restrictions like the autocorrelation function. We describe a Bayesian approach, discuss computational issues, and apply the proposed methods to CD4 count data from a pediatric AIDS clinical trial.
*Details can be found in the paper published in Statistics in Medicine 2015, 34, 2004–2018.

1300 Panel convene

1310-1340 Dr Plamen Turkedjiev

1345-1415 Dr Martin Herdegen

Break 1420-1510

1510-1540 Dr Stefano Pagliarani

1545-1615 Dr Antonis Papapantoleon

1620-1650 Dr Pietro Siopaes

Neil Lawrence (University of Sheffield)
Title: Machine Learning with Gaussian Processes
Abstract: Gaussian processes provide a principled probabilistic approach to prior probability distributions for functions. In this talk we will give an overview of some uses of Gaussian processes and their extensions. In particular we will introduce mechanistic models alongside Gaussian processes and use them within the framework of latent variable models.

Andrew Zisserman (Oxford University)
Title: Deep Learning: Architectures and Applications
Abstract: This talk will have three parts: first, a short review of Convolutional Neural Network (CNN) architectures - what is learnt, and how the performance depends on the depth of the network; second, an application of CNNs to human face recognition by using a classification or embedding loss function; and third, an application of CNNs to the task of regressing human pose in images and videos.

14.00 - 15.00: Neil Lawrence, “Machine Learning with Gaussian Processes”
15.00 - 15.30: Coffee break
15.30 - 16.30: Andrew Zisserman, “Deep Learning: Architectures and Applications”

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

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