Smooth Relevance Vector Machines

Regression tasks belong to the set of core problems faced in statistics and machine learning and promising approaches can often be generalized to also deal with classification, interpolation or denoising problems. Whereas the most widely used classical statistical techniques place severe a priori constraints on the type of function that can be approximated (e.g. only lines, in the case of linear regression), the successes of sparse kernel learners, such as the SVM (support vector machine) demonstrate that good results may be obtained in a quite general framework by enforcing sparsity. Similarly, even very simple sparsity-based denoising techniques, such as classical wavelet shrinkage, can produce surprisingly good results on a wide variety of different signals, because, unlike noise, most signals of practical interest share vital characteristics (such as smoothness, or the ability to be well approximated by piece-wise linear polynomials of a low order) that allow a sparse representation in wavelet space. On the other hand results obtained from SVMs (and classical wavelet-shrinkage) suffer from a certain lack of interpretability, since one cannot straightforwardly attach probabilities to them. By contrast regression, and even more importantly classification, in a Bayesian context always entails a probabilistic measure of confidence in the results, which, provided the model assumptions are reasonably accurate, forms a basis for principled decision-making. The relevance vector machine (RVM) combines these strengths by explicitly encoding the criterion of model sparsity as a (Bayesian) prior over the model weights and offers a single, unified paradigm to efficiently deal with regression as well as classification tasks. However the lack of an explicit prior structure over the weight variances means that the degree of sparsity is to a large extent controlled by the choice of kernel (and kernel parameters). This can lead to severe overfitting or oversmoothing -- possibly even both at the same time (e.g. for the multiscale Doppler data). This thesis details an efficient scheme to control sparsity in Bayesian regression by incorporating a flexible noise-dependent smoothness prior into the RVM. The resultant smooth RVM (sRVM) encompasses the original RVM as a special case, but empirical results with a variety of popular data sets show that it can surpass RVM performance in terms of goodness of fit and achieved sparsity as well as computational performance in many cases. As the smoothness prior effectively makes it possible to use (highly efficient) wavelet kernels in an RVM setting this work also unveils a strong connection between Bayesian wavelet shrinkage and RVM regression and effectively further extends the applicability of the RVM to denoising tasks for up to millions of datapoints. We further discuss its applicability to classification tasks

Views of young people with chronic conditions on transition from pediatric to adult health services

PURPOSE: This study sought to identify and describe the views of young people with chronic conditions about the transition from pediatric to adult services. METHODS: Q methodology was used to identify young people’s views on transition. A set of 39 statements about transition was developed from an existing literature review and refined in consultation with local groups of young people. Statements were printed onto cards and a purposive sample of 44 young people with chronic health conditions was recruited, 41 remaining in the study. The young people were asked to sort the statement cards onto a Q-sort grid, according to their opinions from “strongly disagree” to “strongly agree.” Factor analysis was used to identify shared points of view (patterns of similarity between individual’s Q-sorts). RESULTS: Four distinct views on transition were identified from young people: (1) “a laid-back view of transition;” (2) “anxiety about transition;” (3) “wanting independence and autonomy during transition;” and (4) “valuing social interaction with family, peers, and professionals to assist transition.” CONCLUSIONS: Successful transition is likely to be influenced by how young people view the process. Discussing and understanding young people’s views and preferences about transition should help clinicians and young people develop personalized planning for transition as a whole, and more specifically the point of transfer, leading to effective and efficient engagement with adult care

Public views on principles for health care priority setting: findings of a European cross-country study using Q methodology

International audienc

Who knows best? A Q methodology study to explore perspectives of professional stakeholders and community participants on health in low-income communities

Abstract Background Health inequalities in the UK have proved to be stubborn, and health gaps between best and worst-off are widening. While there is growing understanding of how the main causes of poor health are perceived among different stakeholders, similar insight is lacking regarding what solutions should be prioritised. Furthermore, we do not know the relationship between perceived causes and solutions to health inequalities, whether there is agreement between professional stakeholders and people living in low-income communities or agreement within these groups. Methods Q methodology was used to identify and describe the shared perspectives (‘subjectivities’) that exist on i) why health is worse in low-income communities (‘Causes’) and ii) the ways that health could be improved in these same communities (‘Solutions’). Purposively selected individuals (n = 53) from low-income communities (n = 25) and professional stakeholder groups (n = 28) ranked ordered sets of statements – 34 ‘Causes’ and 39 ‘Solutions’ – onto quasi-normal shaped grids according to their point of view. Factor analysis was used to identify shared points of view. ‘Causes’ and ‘Solutions’ were analysed independently, before examining correlations between perspectives on causes and perspectives on solutions. Results Analysis produced three factor solutions for both the ‘Causes’ and ‘Solutions’. Broadly summarised these accounts for ‘Causes’ are: i) ‘Unfair Society’, ii) ‘Dependent, workless and lazy’, iii) ‘Intergenerational hardships’ and for ‘Solutions’: i) ‘Empower communities’, ii) ‘Paternalism’, iii) ‘Redistribution’. No professionals defined (i.e. had a significant association with one factor only) the ‘Causes’ factor ‘Dependent, workless and lazy’ and the ‘Solutions’ factor ‘Paternalism’. No community participants defined the ‘Solutions’ factor ‘Redistribution’. The direction of correlations between the two sets of factor solutions – ‘Causes’ and ‘Solutions’ – appear to be intuitive, given the accounts identified. Conclusions Despite the plurality of views there was broad agreement across accounts about issues relating to money. This is important as it points a way forward for tackling health inequalities, highlighting areas for policy and future research to focus on

What constitutes a successful biodiversity corridor? A Q-study in the Cape Floristic Region, South Africa

‘Success’ is a vigorously debated concept in conservation. There is a drive to develop quantitative, comparable metrics of success to improve conservation interventions. Yet the qualitative, normative choices inherent in decisions about what to measure — emerging from fundamental philosophical commitments about what conservation is and should be — have received scant attention. We address this gap by exploring perceptions of what constitutes a successful biodiversity corridor in the Cape Floristic Region, South Africa, an area of global biodiversity significance. Biodiversity corridors are particularly illustrative because, as interventions intended to extend conservation practices from protected areas across broader landscapes, they represent prisms in which ideas of conservation success are contested and transformed. We use Q method to elicit framings of success among 20 conservation scientists, practitioners and community representatives, and find three statistically significant framings of successful corridors: ‘a last line of defence for biodiversity under threat,’ ‘a creative process to develop integrative, inclusive visions of biodiversity and human wellbeing,’ and ‘a stimulus for place-based cultural identity and economic development.’ Our results demonstrate that distinct understandings of what a corridor is — a planning tool, a process of governing, a territorialized place — produce divergent framings of ‘successful’ corridors that embody diverse, inherently contestable visions of conservation. These framings emerge from global conservation discourses and distinctly local ecologies, politics, cultures and histories. We conclude that visions of conservation success will be inherently plural, and that in inevitably contested and diverse social contexts success on any terms rests upon recognition of and negotiation with alternative visions