Evaluating Specification Tests for Markov-Switching Time-Series Models

We evaluate the performance of several specification tests for Markov regime-switching time-series models. We consider the Lagrange multiplier (LM) and dynamic specification tests of Hamilton (1996) and Ljung–Box tests based on both the generalized residual and a standard-normal residual constructed using the Rosenblatt transformation. The size and power of the tests are studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung–Box tests exhibit slight size distortions, though tests based on the Rosenblatt transformation perform better than the generalized residual-based tests. The tests exhibit impressive power to detect both autocorrelation and autoregressive conditional heteroscedasticity (ARCH). The tests are illustrated with a Markov-switching generalized ARCH (GARCH) model fitted to the US dollar–British pound exchange rate, with the finding that both autocorrelation and GARCH effects are needed to adequately fit the data.

The influence of travel time and size of shopping center towards the frequencies of visiting customers in shopping centers in surabaya

Visiting a modern shopping center is becoming vital in our society nowadays. The fast growth of shopping center, transportation system, and modern vehicles has given more choices for consumers in shopping. Although there are many reasons for the consumers in visiting the shopping center, the influence of travel time and size of shopping center are important things to be considered towards the frequencies of visiting customers in shopping centers. A survey to the customers of three major shopping centers in Surabaya has been conducted to evaluate the Ellwood’s model and Huff’s model. A new exponent value N of 0.48 and n of 0.50 has been found from the Ellwood’s model, while a coefficient of 0.267 and an add value of 0.245 have been found from the Huff’s model.

Holding cost model for Greenfield Housing developments

Presentation provided to a PhD Colloquium between two Australian and one Malaysian University providing the opportunity to inform and critique progress of students concerning their selected topic. This presentation essentially involves "The conceptualisation, sensitivity and measurement of holding costs and other selected elements impacting housing affordability" as provided by Gary Owen Garner of QUT, with research objectives thus: 1. To establish the nature and composition of holding costs over time, as related to residential property in Australia, and internationally. 2. To examine the linkages that may exist between various planning instruments, the length of regulatory assessment periods, and housing affordability. 3. To develop a model that quantifies the impact of holding costs on housing affordability in Australia, with a particular focus on the consequences of extended assessment periods as a component of holding costs. Thus, provide clarification as to the impact of holding costs on overall housing affordability.

Composite load model decomposition : induction motor contribution

In this thesis, a new technique has been developed for determining the composition of a collection of loads including induction motors. The application would be to provide a representation of the dynamic electrical load of Brisbane so that the ability of the power system to survive a given fault can be predicted. Most of the work on load modelling to date has been on post disturbance analysis, not on continuous on-line models for loads. The post disturbance methods are unsuitable for load modelling where the aim is to determine the control action or a safety margin for a specific disturbance. This thesis is based on on-line load models. Dr. Tania Parveen considers 10 induction motors with different power ratings, inertia and torque damping constants to validate the approach, and their composite models are developed with different percentage contributions for each motor. This thesis also shows how measurements of a composite load respond to normal power system variations and this information can be used to continuously decompose the load continuously and to characterize regarding the load into different sizes and amounts of motor loads.

The Revised Transactional Model (RTM) of occupational stress and coping : an improved process approach

Despite more than three decades of research, there is a limited understanding of the transactional processes of appraisal, stress and coping. This has led to calls for more focused research on the entire process that underlies these variables. To date, there remains a paucity of such research. The present study examined Lazarus and Folkman’s (1984) transactional model of stress and coping. One hundred and twenty nine Australian participants with full time employment (i.e. nurses and administration employees) were recruited. There were 49 male (age mean = 34, SD = 10.51) and 80 female (age mean = 36, SD = 10.31) participants. The analysis of three path models indicated that in addition to the original paths, which were found in Lazarus and Folkman’s transactional model (primary appraisal-->secondary appraisal-->stress-->coping), there were also direct links between primary appraisal and stress level time one and between stress level time one to stress level time two. This study has provided additional insights into the transactional process which will extend our understanding of how individuals appraise, cope and experience occupational stress.

One-time-password-authenticated key exchange

To reduce the damage of phishing and spyware attacks, banks, governments, and other security-sensitive industries are deploying one-time password systems, where users have many passwords and use each password only once. If a single password is compromised, it can be only be used to impersonate the user once, limiting the damage caused. However, existing practical approaches to one-time passwords have been susceptible to sophisticated phishing attacks. ---------- We give a formal security treatment of this important practical problem. We consider the use of one-time passwords in the context of password-authenticated key exchange (PAKE), which allows for mutual authentication, session key agreement, and resistance to phishing attacks. We describe a security model for the use of one-time passwords, explicitly considering the compromise of past (and future) one-time passwords, and show a general technique for building a secure one-time-PAKE protocol from any secure PAKE protocol. Our techniques also allow for the secure use of pseudorandomly generated and time-dependent passwords.

Model round-trip engineering

In the quest for shorter time-to-market, higher quality and reduced cost, model-driven software development has emerged as a promising approach to software engineering. The central idea is to promote models to first-class citizens in the development process. Starting from a set of very abstract models in the early stage of the development, they are refined into more concrete models and finally, as a last step, into code. As early phases of development focus on different concepts compared to later stages, various modelling languages are employed to most accurately capture the concepts and relations under discussion. In light of this refinement process, translating between modelling languages becomes a time-consuming and error-prone necessity. This is remedied by model transformations providing support for reusing and automating recurring translation efforts. These transformations typically can only be used to translate a source model into a target model, but not vice versa. This poses a problem if the target model is subject to change. In this case the models get out of sync and therefore do not constitute a coherent description of the software system anymore, leading to erroneous results in later stages. This is a serious threat to the promised benefits of quality, cost-saving, and time-to-market. Therefore, providing a means to restore synchronisation after changes to models is crucial if the model-driven vision is to be realised. This process of reflecting changes made to a target model back to the source model is commonly known as Round-Trip Engineering (RTE). While there are a number of approaches to this problem, they impose restrictions on the nature of the model transformation. Typically, in order for a transformation to be reversed, for every change to the target model there must be exactly one change to the source model. While this makes synchronisation relatively “easy”, it is ill-suited for many practically relevant transformations as they do not have this one-to-one character. To overcome these issues and to provide a more general approach to RTE, this thesis puts forward an approach in two stages. First, a formal understanding of model synchronisation on the basis of non-injective transformations (where a number of different source models can correspond to the same target model) is established. Second, detailed techniques are devised that allow the implementation of this understanding of synchronisation. A formal underpinning for these techniques is drawn from abductive logic reasoning, which allows the inference of explanations from an observation in the context of a background theory. As non-injective transformations are the subject of this research, there might be a number of changes to the source model that all equally reflect a certain target model change. To help guide the procedure in finding “good” source changes, model metrics and heuristics are investigated. Combining abductive reasoning with best-first search and a “suitable” heuristic enables efficient computation of a number of “good” source changes. With this procedure Round-Trip Engineering of non-injective transformations can be supported.

Modeling multivariate autoregressive conditional heteroskedasticity with the double smooth transition conditional correlation GARCH Model

In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Teräsvirta (2005) by including another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. Applying the model to the stock and bond futures data, we discover that the correlation pattern between them has dramatically changed around the turn of the century. The model is also applied to a selection of world stock indices, and we find evidence for an increasing degree of integration in the capital markets.

Health, endogenous time preference, and inequality : a political economy perspective

We study an overlapping-generations model in which agents' mortality risks, and consequently impatience, are endogenously determined by private and public investment in health care. Revenues allocated for public health care arc determined by a voting process. We find that the degree of substitutability between public and private health expenditures matters for macroeconomic outcomes of the model. Higher substitutability implies a “crowding-out" effect, which in turn impacts adversely on morality risks and impatience leading to lower public expenditures on health care in the political equilibrium. Consequently, higher substitutability is associated with greater polarization in wealth, and long-run distributions that are bimodal.

Testing a tri-partite contingent model of engineering cultures : a pilot study

For some time there has been a growing awareness of organizational culture and its impact on the functioning of engineering and maintenance departments. Those wishing to implement contemporary maintenance regimes (e.g. condition based maintenance) are often encouraged to develop “appropriate cultures” to support a new method’s introduction. Unfortunately these same publications often fail to specifically articulate the cultural values required to support those efforts. In the broader literature, only a limited number of case examples document the cultural values held by engineering asset intensive firms and how they contribute to their success (or failure). Consequently a gap exists in our knowledge of what engineering cultures currently might look like, or what might constitute a best practice engineering asset culture. The findings of a pilot study investigating the perceived ideal characteristics of engineering asset cultures are reported. Engineering managers, consultants and academics (n=47), were surveyed as to what they saw were essential attributes of both engineering cultures and engineering asset personnel. Valued cultural elements included those orientated around continuous improvement, safety and quality. Valued individual attributes included openness to change, interpersonal skills and conscientiousness. The paper concludes with a discussion regarding the development of a best practice cultural framework for practitioners and engineering managers.

An application of Keller’s Brand Equity Model in a B2B context

Powerful brands create meaningful images in the minds of customers (Keller, 1993). A strong brand image and reputation enhances differentiation and has a positive influence on buying behaviour (Gordon et al., 1993; McEnally and de Chernatony, 1999). While the power of branding is widely acknowledged in consumer markets, the nature and importance of branding in industrial markets remains under-researched. Many business-to-business (B2B) strategists have claimed brand-building belongs in the consumer realm. They argue that industrial products do not need branding as it is confusing and adds little value to functional products (Collins, 1977; Lorge, 1998; Saunders and Watt, 1979). Others argue that branding and the concept of brand equity however are increasingly important in industrial markets, because it has been shown that what a brand means to a buyer can be a determining factor in deciding between industrial purchase alternatives (Aaker, 1991). In this context, it is critical for suppliers to initiate and sustain relationships due to the small number of potential customers (Ambler, 1995; Webster and Keller, 2004). To date however, there is no model available to assist B2B marketers in identifying and measuring brand equity. In this paper, we take a step in that direction by operationalising and empirically testing a prominent brand equity model in a B2B context. This makes not only a theoretical contribution by advancing branding research, but also addresses a managerial need for information that will assist in the assessment of industrial branding efforts.

Understanding the Aquifier : 3D Visualisation and Groundwater Time-Series Animation in Lockyer Valley

The Lockyer Valley, southeast Queensland, hosts intensive irrigated agriculture using groundwater from over 5000 alluvial bores. A current project is considering introduction of PRW (purified recycled water) to augment groundwater supplies. To assess this, a valley-wide MODFLOW simulation model is being developed plus a new unsaturated zone flow model. To underpin these models and provide a realistic understanding of the aquifer framework a 3D visualisation model has been developed using Groundwater Visualisation System (GVS) software produced at QUT.

Port of Brisbane stormwater quality : stage 2 development of a port specific water quality model

Background: The quality of stormwater runoff from ports is significant as it can be an important source of pollution to the marine environment. This is also a significant issue for the Port of Brisbane as it is located in an area of high environmental values. Therefore, it is imperative to develop an in-depth understanding of stormwater runoff quality to ensure that appropriate strategies are in place for quality improvement, where necessary. To this end, the Port of Brisbane Corporation aimed to develop a port specific stormwater model for the Fisherman Islands facility. The need has to be considered in the context of the proposed future developments of the Port area. ----------------- The Project: The research project is an outcome of the collaborative Partnership between the Port of Brisbane Corporation (POBC) and Queensland University of Technology (QUT). A key feature of this Partnership is that it seeks to undertake research to assist the Port in strengthening the environmental custodianship of the Port area through ‘cutting edge’ research and its translation into practical application. ------------------ The project was separated into two stages. The first stage developed a quantitative understanding of the generation potential of pollutant loads in the existing land uses. This knowledge was then used as input for the stormwater quality model developed in the subsequent stage. The aim is to expand this model across the yet to be developed port expansion area. This is in order to predict pollutant loads associated with stormwater flows from this area with the longer term objective of contributing to the development of ecological risk mitigation strategies for future expansion scenarios. ----------------- Study approach: Stage 1 of the overall study confirmed that Port land uses are unique in terms of the anthropogenic activities occurring on them. This uniqueness in land use results in distinctive stormwater quality characteristics different to other conventional urban land uses. Therefore, it was not scientifically valid to consider the Port as belonging to a single land use category or to consider as being similar to any typical urban land use. The approach adopted in this study was very different to conventional modelling studies where modelling parameters are developed using calibration. The field investigations undertaken in Stage 1 of the overall study helped to create fundamental knowledge on pollutant build-up and wash-off in different Port land uses. This knowledge was then used in computer modelling so that the specific characteristics of pollutant build-up and wash-off can be replicated. This meant that no calibration processes were involved due to the use of measured parameters for build-up and wash-off. ---------------- Conclusions: Stage 2 of the study was primarily undertaken using the SWMM stormwater quality model. It is a physically based model which replicates natural processes as closely as possible. The time step used and catchment variability considered was adequate to accommodate the temporal and spatial variability of input parameters and the parameters used in the modelling reflect the true nature of rainfall-runoff and pollutant processes to the best of currently available knowledge. In this study, the initial loss values adopted for the impervious surfaces are relatively high compared to values noted in research literature. However, given the scientifically valid approach used for the field investigations, it is appropriate to adopt the initial losses derived from this study for future modelling of Port land uses. The relatively high initial losses will reduce the runoff volume generated as well as the frequency of runoff events significantly. Apart from initial losses, most of the other parameters used in SWMM modelling are generic to most modelling studies. Development of parameters for MUSIC model source nodes was one of the primary objectives of this study. MUSIC, uses the mean and standard deviation of pollutant parameters based on a normal distribution. However, based on the values generated in this study, the variation of Event Mean Concentrations (EMCs) for Port land uses within the given investigation period does not fit a normal distribution. This is possibly due to the fact that only one specific location was considered, namely the Port of Brisbane unlike in the case of the MUSIC model where a range of areas with different geographic and climatic conditions were investigated. Consequently, the assumptions used in MUSIC are not totally applicable for the analysis of water quality in Port land uses. Therefore, in using the parameters included in this report for MUSIC modelling, it is important to note that it may result in under or over estimations of annual pollutant loads. It is recommended that the annual pollutant load values given in the report should be used as a guide to assess the accuracy of the modelling outcomes. A step by step guide for using the knowledge generated from this study for MUSIC modelling is given in Table 4.6. ------------------ Recommendations: The following recommendations are provided to further strengthen the cutting edge nature of the work undertaken: * It is important to further validate the approach recommended for stormwater quality modelling at the Port. Validation will require data collection in relation to rainfall, runoff and water quality from the selected Port land uses. Additionally, the recommended modelling approach could be applied to a soon-to-be-developed area to assess ‘before’ and ‘after’ scenarios. * In the modelling study, TSS was adopted as the surrogate parameter for other pollutants. This approach was based on other urban water quality research undertaken at QUT. The validity of this approach should be further assessed for Port land uses. * The adoption of TSS as a surrogate parameter for other pollutants and the confirmation that the <150 m particle size range was predominant in suspended solids for pollutant wash-off gives rise to a number of important considerations. The ability of the existing structural stormwater mitigation measures to remove the <150 m particle size range need to be assessed. The feasibility of introducing source control measures as opposed to end-of-pipe measures for stormwater quality improvement may also need to be considered.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

A hidden Markov model and relative entropy rate approach to vision-based dim target detection for UAV sense-and-avoid

Uninhabited aerial vehicles (UAVs) are a cutting-edge technology that is at the forefront of aviation/aerospace research and development worldwide. Many consider their current military and defence applications as just a token of their enormous potential. Unlocking and fully exploiting this potential will see UAVs in a multitude of civilian applications and routinely operating alongside piloted aircraft. The key to realising the full potential of UAVs lies in addressing a host of regulatory, public relation, and technological challenges never encountered be- fore. Aircraft collision avoidance is considered to be one of the most important issues to be addressed, given its safety critical nature. The collision avoidance problem can be roughly organised into three areas: 1) Sense; 2) Detect; and 3) Avoid. Sensing is concerned with obtaining accurate and reliable information about other aircraft in the air; detection involves identifying potential collision threats based on available information; avoidance deals with the formulation and execution of appropriate manoeuvres to maintain safe separation. This thesis tackles the detection aspect of collision avoidance, via the development of a target detection algorithm that is capable of real-time operation onboard a UAV platform. One of the key challenges of the detection problem is the need to provide early warning. This translates to detecting potential threats whilst they are still far away, when their presence is likely to be obscured and hidden by noise. Another important consideration is the choice of sensors to capture target information, which has implications for the design and practical implementation of the detection algorithm. The main contributions of the thesis are: 1) the proposal of a dim target detection algorithm combining image morphology and hidden Markov model (HMM) filtering approaches; 2) the novel use of relative entropy rate (RER) concepts for HMM filter design; 3) the characterisation of algorithm detection performance based on simulated data as well as real in-flight target image data; and 4) the demonstration of the proposed algorithm's capacity for real-time target detection. We also consider the extension of HMM filtering techniques and the application of RER concepts for target heading angle estimation. In this thesis we propose a computer-vision based detection solution, due to the commercial-off-the-shelf (COTS) availability of camera hardware and the hardware's relatively low cost, power, and size requirements. The proposed target detection algorithm adopts a two-stage processing paradigm that begins with an image enhancement pre-processing stage followed by a track-before-detect (TBD) temporal processing stage that has been shown to be effective in dim target detection. We compare the performance of two candidate morphological filters for the image pre-processing stage, and propose a multiple hidden Markov model (MHMM) filter for the TBD temporal processing stage. The role of the morphological pre-processing stage is to exploit the spatial features of potential collision threats, while the MHMM filter serves to exploit the temporal characteristics or dynamics. The problem of optimising our proposed MHMM filter has been examined in detail. Our investigation has produced a novel design process for the MHMM filter that exploits information theory and entropy related concepts. The filter design process is posed as a mini-max optimisation problem based on a joint RER cost criterion. We provide proof that this joint RER cost criterion provides a bound on the conditional mean estimate (CME) performance of our MHMM filter, and this in turn establishes a strong theoretical basis connecting our filter design process to filter performance. Through this connection we can intelligently compare and optimise candidate filter models at the design stage, rather than having to resort to time consuming Monte Carlo simulations to gauge the relative performance of candidate designs. Moreover, the underlying entropy concepts are not constrained to any particular model type. This suggests that the RER concepts established here may be generalised to provide a useful design criterion for multiple model filtering approaches outside the class of HMM filters. In this thesis we also evaluate the performance of our proposed target detection algorithm under realistic operation conditions, and give consideration to the practical deployment of the detection algorithm onboard a UAV platform. Two fixed-wing UAVs were engaged to recreate various collision-course scenarios to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. Based on this collected data, our proposed detection approach was able to detect targets out to distances ranging from about 400m to 900m. These distances, (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning ahead of impact that approaches the 12.5 second response time recommended for human pilots. Furthermore, readily available graphic processing unit (GPU) based hardware is exploited for its parallel computing capabilities to demonstrate the practical feasibility of the proposed target detection algorithm. A prototype hardware-in- the-loop system has been found to be capable of achieving data processing rates sufficient for real-time operation. There is also scope for further improvement in performance through code optimisations. Overall, our proposed image-based target detection algorithm offers UAVs a cost-effective real-time target detection capability that is a step forward in ad- dressing the collision avoidance issue that is currently one of the most significant obstacles preventing widespread civilian applications of uninhabited aircraft. We also highlight that the algorithm development process has led to the discovery of a powerful multiple HMM filtering approach and a novel RER-based multiple filter design process. The utility of our multiple HMM filtering approach and RER concepts, however, extend beyond the target detection problem. This is demonstrated by our application of HMM filters and RER concepts to a heading angle estimation problem.