Bootstrap approaches for estimation and confidence intervals of long term memory processes

In this work, we investigate an alternative bootstrap approach based on a result of Ramsey [F.L. Ramsey, Characterization of the partial autocorrelation function, Ann. Statist. 2 (1974), pp. 1296-1301] and on the Durbin-Levinson algorithm to obtain a surrogate series from linear Gaussian processes with long range dependence. We compare this bootstrap method with other existing procedures in a wide Monte Carlo experiment by estimating, parametrically and semi-parametrically, the memory parameter d. We consider Gaussian and non-Gaussian processes to prove the robustness of the method to deviations from normality. The approach is also useful to estimate confidence intervals for the memory parameter d by improving the coverage level of the interval.

Jack's Bay (the architecturalisation of memory)

Jack's Bay (the architecturalisation of memory) is a key work of the author's exhibition Lightsite, which toured Western Australian galleries from February 2006 to November 2007. It is a five-minute-long exposure photographic image captured inside a purpose-built, room-sized pinhole camera which is demountable and does not have a floor. The work depicts octogenarian Jack Morris, who for forty years held the professional salmon fishing license in the hamlet of Bremer Bay, on the SE coast of Western Australia. The pinhole camera-room is sited within sand dunes new Jack's now demolished beachside camp. Three generations of Jack's descendents stand outside the room - from his daughter to his great grand children. The light from this exterior landscape is 'projected' inside the camera-room and illuminates the interior scene which includes that part of the sand dune upon which the floorless room is erected, along with Jack who is sitting inside. The image evokes the temporality of light. Here, light itself is portrayed as the primary medium through which we both perceive and describe landscape. In this way it is through the agency of light that we construct our connectivity to landscape.

Fusionless scoliosis correction using shape memory alloy staples

Severe spinal deformity in young children is a formidable challenge for optimal treatment. Standard interventions for adolescents, such as spinal deformity correction and fusion, may not be appropriate for young patients with considerable growth remaining. Alternative surgical options that provide deformity correction and protect the growth remaining in the spine are needed to treat this group of patients 1, 2. One such method is the use of shape memory alloy staples. We report our experience to date using video-assisted thoracoscopic insertion of shape memory alloy staples. A retrospective review was conducted of 13 patients with scoliosis, aged 7 to 13 years, who underwent video-assisted thoracoscopic insertion of shape memory staples. In our experience, video-assisted thoracoscopic insertion of shape memory alloy staples is a safe procedure with no complications noted. It is a reliable method of providing curve stability, however the follow up results to date indicate that the effectiveness of the procedure is greater in younger patients.

Fuzzy associative memory for humanoid robot joint control

Traditional approaches to joint control required accurate modelling of the system dynamic of the plant in question. Fuzzy Associative Memory (FAM) control schemes allow adequate control without a model of the system to be controlled. This paper presents a FAM based joint controller implemented on a humanoid robot. An empirically tuned PI velocity control loop is augmented with this feed forward FAM, with considerable reduction in joint position error achieved online and with minimal additional computational overhead.

Mediatisation and institutions of public memory : digital storytelling and the apology

Institutions of public memory are increasingly undertaking co-creative media initiatives in which community members create content with the support of institutional expertise and resources. This paper discusses one such initiative: the State Library of Queensland’s ‘Responses to the Apology’, which used a collaborative digital storytelling methodology to co-produce seven short videos capturing individual responses to Prime Minister Kevin Rudd’s 2008 ‘Apology to Australia’s Indigenous Peoples’. In examining this program, we are interested not only in the juxtaposition of ‘ordinary’ responses to an ‘official’ event, but also in how the production and display of these stories might also demonstrate a larger mediatisation of public memory.

Bayesian models for longitudinal data

Longitudinal data, where data are repeatedly observed or measured on a temporal basis of time or age provides the foundation of the analysis of processes which evolve over time, and these can be referred to as growth or trajectory models. One of the traditional ways of looking at growth models is to employ either linear or polynomial functional forms to model trajectory shape, and account for variation around an overall mean trend with the inclusion of random eects or individual variation on the functional shape parameters. The identification of distinct subgroups or sub-classes (latent classes) within these trajectory models which are not based on some pre-existing individual classification provides an important methodology with substantive implications. The identification of subgroups or classes has a wide application in the medical arena where responder/non-responder identification based on distinctly diering trajectories delivers further information for clinical processes. This thesis develops Bayesian statistical models and techniques for the identification of subgroups in the analysis of longitudinal data where the number of time intervals is limited. These models are then applied to a single case study which investigates the neuropsychological cognition for early stage breast cancer patients undergoing adjuvant chemotherapy treatment from the Cognition in Breast Cancer Study undertaken by the Wesley Research Institute of Brisbane, Queensland. Alternative formulations to the linear or polynomial approach are taken which use piecewise linear models with a single turning point, change-point or knot at a known time point and latent basis models for the non-linear trajectories found for the verbal memory domain of cognitive function before and after chemotherapy treatment. Hierarchical Bayesian random eects models are used as a starting point for the latent class modelling process and are extended with the incorporation of covariates in the trajectory profiles and as predictors of class membership. The Bayesian latent basis models enable the degree of recovery post-chemotherapy to be estimated for short and long-term followup occasions, and the distinct class trajectories assist in the identification of breast cancer patients who maybe at risk of long-term verbal memory impairment.

Wellness in older adults

Understanding perception of wellness in older adults is a question to be understood against the backdrop of concerns about whether global ageing and the ‘bulge’ of ageing baby boomers will increase health care cost beyond what modern economies can deal with. Older adults who age in a healthy way and who take responsibility for their own health offer a positive alternative and change the perception that older adults are a burden on their society’s health system. The concept of successful ageing introduced by Rowe and Kahn (1987; 1997) suggested that older adults age successfully if they avoid disease and disability, maintain high cognitive and physical functioning and remain actively engaged with life. This concept, however, did not reflect older adults’ own perceptions of what constitutes successful ageing or how perceptions of wellness or health-related quality of life influenced the older adult’s understanding of his or her own health and ageing. A research project was designed to examine older adults’ perceptions of wellness in order to gain an understanding of the factors that influence perception of their own wellness. Specifically, the research wanted to explore two aspects: whether belonging to a unique organisation, in this instance a Returned Services Club, influenced perceptions of wellness; and whether there are significant gender differences for the perception of wellness. A mixed method project with two consecutive studies was designed to answer these questions: a quantitative survey of members of a Returned Services Club and of the surrounding community in Queensland, Australia, and a qualitative study conducting focus groups to explore findings of the survey. The results of the survey were used to determine the composition of the focus groups. The participants for the first study, (N=257), community living adults 65 years and older, were chosen from the membership role of a Returned Services Club or recruited by personal approach from the community surrounding the Services Club. Participants completed a survey that consisted of a perception of wellness instrument, a health-related quality of life instrument, and questions on morbidities, modifiable life style factors and demographics. Data analysis found that a number of individual factors influenced perception of wellness and health-related quality of life. Positive influences were independent mobility, exercise and gambling at non-hazardous levels, and negative influences were hearing loss, memory problems, chronic disease and being single. Membership of the Services Club did not contribute to perception of wellness beyond being a member of a social group. While there may have been an expectation that members of an organisation that is traditionally associated with high alcohol use and problematic gambling may have lower perceptions of wellness, this study suggested that the negative influences may have been counteracted by the positive effects of social interaction, thus having neither negative nor positive influences on perception of wellness. There were significant differences in perception of wellness and in health-related quality of life for women and men. The most significant difference was for women aged 85-90 who had significantly lower scores for perception of wellness than men or than any other age group. This result was the impetus for conducting focus groups with adults aged 85-90 years of age. Focus groups were conducted with 24 women and four men aged 85-90 to explore the survey findings for this age group. Results from the focus groups indicated that for older adults perception of wellness was a multidimensional construct of more complexity than indicated by the survey instrument. Elite older women (women over 85 years of age) related their perception of wellness to their ability to do what they wanted to do, and what they wanted to do significantly more than anything else, was to stay connected to family, friends and the community to which they belonged. From the focus group results it appeared that elite older women identified with the three elements of successful ageing – low incidence of disability and disease, high physical and cognitive functioning, and active engagement with life – but not in a flat structure. It appears that for elite older women good physical and mental health function to enable social connectedness. It is the elements of health that impact on the ability to do what they wanted to do that were identified as key factors: independent mobility, hearing and memory - factors that impact on the ability to interact socially. These elements were only identified when they impacted on the person’s ability to do what they wanted to do, for example mobility problems that were managed were not considered a problem. The study also revealed that older women use selection, optimisation and compensation to meet their goal of staying socially connected. The shopping centre was a key factor in this goal and older women used shopping centres to stay connected to the community and for exercise as well as shopping. Personal and public safety and other environmental concerns were viewed in the same context of enabling or disabling social connectedness. This suggested that for elite older women the model of successful ageing was hierarchical rather than flat, with social connectedness at the top, supported by cognitive functioning and good physical and mental health. In conclusion, this research revealed that perception of wellness in older adults is a complex, multidimensional construct. For older adults good health is related to social connectedness and is not a goal in itself. Health professionals and the community at large have a responsibility to take into account the ability of the older adult to stay socially connected to their community and to enable this, if the goal is to keep older adults healthy for as long as possible. Maintaining or improving perception of wellness in older adults will require a broad biopsychosocial approach that utilises findings such as older adults’ use of shopping centres for non-shopping purposes, concerns about personal and environmental safety and supporting older adults to maintain or improve their social connectedness to their communities.

Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour

This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult problem. Difficulty with estimating the normalization constant for the MRF can be overcome by using a path integral approach, although this is a highly computationally intensive method. Different methods of estimating ratios of normalization constants (N Cs) are investigated, including importance sampling Monte Carlo (ISMC), dependent Monte Carlo based on MCMC simulations (MCMC), and reverse logistic regression (RLR). I develop an idea present though not fully developed in the literature, and propose the Integrated mean canonical statistic (IMCS) method for estimating log NC ratios for binary MRFs. The IMCS method falls within the framework of the newly identified path sampling methods of Gelman & Meng (1998) and outperforms ISMC, MCMC and RLR. It also does not rely on simplifying assumptions, such as ignoring spatio-temporal dependence in the process. A thorough investigation is made of the application of IMCS to the three-parameter Autologistic model. This work introduces background computations required for the full implementation of the four-tier model in Chapter 7. Two different extensions of the three-tier model to a four-tier version are investigated. The first extension incorporates temporal dependence in the underlying spatio-temporal process. The second extensions allows the successes and failures in the data layer to depend on time. The MCMC computational method is extended to incorporate the extra layer. A major contribution of the thesis is the development of a fully Bayesian approach to inference for these hierarchical models for the first time. Note: The author of this thesis has agreed to make it open access but invites people downloading the thesis to send her an email via the 'Contact Author' function.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Hierarchical fuzzy logic traffic control at a road junction

Traffic control at road junctions is one of the major concerns in most metropolitan cities. Controllers of various approaches are available and the required control action is the effective green-time assigned to each traffic stream within a traffic-light cycle. The application of fuzzy logic provides the controller with the capability to handle uncertain natures of the system, such as drivers’ behaviour and random arrivals of vehicles. When turning traffic is allowed at the junction, the number of phases in the traffic-light cycle increases. The additional input variables inevitably complicate the controller and hence slow down the decision-making process, which is critical in this real-time control problem. In this paper, a hierarchical fuzzy logic controller is proposed to tackle this traffic control problem at a 2-way road junction with turning traffic. The two levels of fuzzy logic controllers devise the minimum effective green-time and fine-tune it respectively at each phase of a traffic-light cycle. The complexity of the controller at each level is reduced with smaller rule-set. The performance of this hierarchical controller is examined by comparison with a fixed-time controller under various traffic conditions. Substantial delay reduction has been achieved as a result and the performance and limitation of the controller will be discussed.

Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms

Traffic control at a road junction by a complex fuzzy logic controller is investigated. The increase in the complexity of junction means more number of input variables must be taken into account, which will increase the number of fuzzy rules in the system. A hierarchical fuzzy logic controller is introduced to reduce the number of rules. Besides, the increase in the complexity of the controller makes formulation of the fuzzy rules difficult. A genetic algorithm based off-line leaning algorithm is employed to generate the fuzzy rules. The learning algorithm uses constant flow-rates as training sets. The system is tested by both constant and time-varying flow-rates. Simulation results show that the proposed controller produces lower average delay than a fixed-time controller does under various traffic conditions.

Modular transactional memory

Software transactional memory has the potential to greatly simplify development of concurrent software, by supporting safe composition of concurrent shared-state abstractions. However, STM semantics are defined in terms of low-level reads and writes on individual memory locations, so implementations are unable to take advantage of the properties of user-defined abstractions. Consequently, the performance of transactions over some structures can be disappointing. ----- ----- We present Modular Transactional Memory, our framework which allows programmers to extend STM with concurrency control algorithms tailored to the data structures they use in concurrent programs. We describe our implementation in Concurrent Haskell, and two example structures: a finite map which allows concurrent transactions to operate on disjoint sets of keys, and a non-deterministic channel which supports concurrent sources and sinks. ----- ----- Our approach is based on previous work by others on boosted and open-nested transactions, with one significant development: transactions are given types which denote the concurrency control algorithms they employ. Typed transactions offer a higher level of assurance for programmers reusing transactional code, and allow more flexible abstract concurrency control.