The efficacy of the Sortino ratio and other benchmarked performance measures under skewed return distributions

This paper will investigate the suitability of existing performance measures under the assumption of a clearly defined benchmark. A range of measures are examined including the Sortino Ratio, the Sharpe Selection ratio (SSR), the Student’s t-test and a decay rate measure. A simulation study is used to assess the power and bias of these measures based on variations in sample size and mean performance of two simulated funds. The Sortino Ratio is found to be the superior performance measure exhibiting more power and less bias than the SSR when the distribution of excess returns are skewed.

Demographic category membership and leadership in small groups : a social identity analysis

Developing the social identity theory of leadership (e.g., [Hogg, M. A. (2001). A social identity theory of leadership. Personality and Social Psychology Review, 5, 184–200]), an experiment (N=257) tested the hypothesis that as group members identify more strongly with their group (salience) their evaluations of leadership effectiveness become more strongly influenced by the extent to which their demographic stereotype-based impressions of their leader match the norm of the group (prototypicality). Participants, with more or less traditional gender attitudes (orientation), were members, under high or low group salience conditions (salience), of non-interactive laboratory groups that had “instrumental” or “expressive” group norms (norm), and a male or female leader (leader gender). As predicted, these four variables interacted significantly to affect perceptions of leadership effectiveness. Reconfiguration of the eight conditions formed by orientation, norm and leader gender produced a single prototypicality variable. Irrespective of participant gender, prototypical leaders were considered more effective in high then low salience groups, and in high salience groups prototypical leaders were more effective than less prototypical leaders. Alternative explanations based on status characteristics and role incongruity theory do not account well for the findings. Implications of these results for the glass ceiling effect and for a wider social identity analysis of the impact of demographic group membership on leadership in small groups are discussed.

Integrated Bayesian network frameworks for modelling complex ecological issues

Ecological problems are typically multi faceted and need to be addressed from a scientific and a management perspective. There is a wealth of modelling and simulation software available, each designed to address a particular aspect of the issue of concern. Choosing the appropriate tool, making sense of the disparate outputs, and taking decisions when little or no empirical data is available, are everyday challenges facing the ecologist and environmental manager. Bayesian Networks provide a statistical modelling framework that enables analysis and integration of information in its own right as well as integration of a variety of models addressing different aspects of a common overall problem. There has been increased interest in the use of BNs to model environmental systems and issues of concern. However, the development of more sophisticated BNs, utilising dynamic and object oriented (OO) features, is still at the frontier of ecological research. Such features are particularly appealing in an ecological context, since the underlying facts are often spatial and temporal in nature. This thesis focuses on an integrated BN approach which facilitates OO modelling. Our research devises a new heuristic method, the Iterative Bayesian Network Development Cycle (IBNDC), for the development of BN models within a multi-field and multi-expert context. Expert elicitation is a popular method used to quantify BNs when data is sparse, but expert knowledge is abundant. The resulting BNs need to be substantiated and validated taking this uncertainty into account. Our research demonstrates the application of the IBNDC approach to support these aspects of BN modelling. The complex nature of environmental issues makes them ideal case studies for the proposed integrated approach to modelling. Moreover, they lend themselves to a series of integrated sub-networks describing different scientific components, combining scientific and management perspectives, or pooling similar contributions developed in different locations by different research groups. In southern Africa the two largest free-ranging cheetah (Acinonyx jubatus) populations are in Namibia and Botswana, where the majority of cheetahs are located outside protected areas. Consequently, cheetah conservation in these two countries is focussed primarily on the free-ranging populations as well as the mitigation of conflict between humans and cheetahs. In contrast, in neighbouring South Africa, the majority of cheetahs are found in fenced reserves. Nonetheless, conflict between humans and cheetahs remains an issue here. Conservation effort in South Africa is also focussed on managing the geographically isolated cheetah populations as one large meta-population. Relocation is one option among a suite of tools used to resolve human-cheetah conflict in southern Africa. Successfully relocating captured problem cheetahs, and maintaining a viable free-ranging cheetah population, are two environmental issues in cheetah conservation forming the first case study in this thesis. The second case study involves the initiation of blooms of Lyngbya majuscula, a blue-green algae, in Deception Bay, Australia. L. majuscula is a toxic algal bloom which has severe health, ecological and economic impacts on the community located in the vicinity of this algal bloom. Deception Bay is an important tourist destination with its proximity to Brisbane, Australia’s third largest city. Lyngbya is one of several algae considered to be a Harmful Algal Bloom (HAB). This group of algae includes other widespread blooms such as red tides. The occurrence of Lyngbya blooms is not a local phenomenon, but blooms of this toxic weed occur in coastal waters worldwide. With the increase in frequency and extent of these HAB blooms, it is important to gain a better understanding of the underlying factors contributing to the initiation and sustenance of these blooms. This knowledge will contribute to better management practices and the identification of those management actions which could prevent or diminish the severity of these blooms.

Methods and preliminary results for a data linkage project to determine long-term survival after intensive care unit admission

Aims: To describe a local data linkage project to match hospital data with the Australian Institute of Health and Welfare (AIHW) National Death Index (NDI) to assess longterm outcomes of intensive care unit patients. Methods: Data were obtained from hospital intensive care and cardiac surgery databases on all patients aged 18 years and over admitted to either of two intensive care units at a tertiary-referral hospital between 1 January 1994 and 31 December 2005. Date of death was obtained from the AIHW NDI by probabilistic software matching, in addition to manual checking through hospital databases and other sources. Survival was calculated from time of ICU admission, with a censoring date of 14 February 2007. Data for patients with multiple hospital admissions requiring intensive care were analysed only from the first admission. Summary and descriptive statistics were used for preliminary data analysis. Kaplan-Meier survival analysis was used to analyse factors determining long-term survival. Results: During the study period, 21 415 unique patients had 22 552 hospital admissions that included an ICU admission; 19 058 surgical procedures were performed with a total of 20 092 ICU admissions. There were 4936 deaths. Median follow-up was 6.2 years, totalling 134 203 patient years. The casemix was predominantly cardiac surgery (80%), followed by cardiac medical (6%), and other medical (4%). The unadjusted survival at 1, 5 and 10 years was 97%, 84% and 70%, respectively. The 1-year survival ranged from 97% for cardiac surgery to 36% for cardiac arrest. An APACHE II score was available for 16 877 patients. In those discharged alive from hospital, the 1, 5 and 10-year survival varied with discharge location. Conclusions: ICU-based linkage projects are feasible to determine long-term outcomes of ICU patients

Do warnings deter rather than produce more sophisticated malingering?

Following Youngjohn, Lees-Haley, and Binder's (1999) comment on Johnson and Lesniak-Karpiak's (1997) study that warnings lead to more subtle malingering, researchers have sought to better understand warning effects. However, such studies have been largely atheoretical and may have confounded warning and coaching. This study examined the effect on malingering of a warning that was based on criminological-sociological concepts derived from the rational choice model of deterrence theory. A total of 78 participants were randomly assigned to a control group, an unwarned simulator group, or one of two warned simulator groups. The warning groups comprised low- and high-level conditions depending on warning intensity. Simulator participants received no coaching about how to fake tests. Outcome variables were scores derived from the Test of Memory Malingering and Wechsler Memory Scale-III. When the rate of malingering was compared across the four groups, a high-level warning effect was found such that warned participants were significantly less likely to exaggerate than unwarned simulators. In an exploratory follow-up analysis, the warned groups were divided into those who reported malingering and those who did not report malingering, and the performance of these groups was compared to that of unwarned simulators and controls. Using this approach, results showed that participants who were deterred from malingering by warning performed no worse than controls. However, on a small number of tests, self-reported malingerers in the low-level warning group appeared less impaired than unwarned simulators. This pattern was not observed in the high-level warning condition. Although cautious interpretation of findings is necessitated by the exploratory nature of some analyses, overall results suggest that using a carefully designed warning may be useful for reducing the rate of malingering. The combination of some noteworthy effect sizes, despite low power and the small size of some groups, suggests that further investigation of the effects of warnings needs to continue to determine their effect more fully.

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.

Effects of an acute α-lactalbumin manipulation on mood and food hedonics in high- and low-trait anxiety individuals

Serotonergic hypofunction is associated with a depressive mood state, an increased drive to eat and preference for sweet (SW) foods. High-trait anxiety individuals are characterised by a functional shortage of serotonin during stress, which in turn increases their susceptibility to experience a negative mood and an increased drive for SW foods. The present study examined whether an acute dietary manipulation, intended to increase circulating serotonin levels, alleviated the detrimental effects of a stress-inducing task on subjective appetite and mood sensations, and preference for SW foods in high-trait anxiety individuals. Thirteen high- (eleven females and two males; anxiety scores 45·5 (sd 5·9); BMI 22·9 (sd 3·0)kg/m2) and twelve low- (ten females and two males; anxiety scores 30·4 (sd 4·8); BMI 23·4 (sd 2·5) kg/m2) trait anxiety individuals participated in a placebo-controlled, two-way crossover design. Participants were provided with 40 g α-lactalbumin (LAC; l-tryptophan (Trp):large neutral amino acids (LNAA) ratio of 7·6) and 40 g casein (placebo) (Trp:LNAA ratio of 4·0) in the form of a snack and lunch on two test days. On both the test days, participants completed a stress-inducing task 2 h after the lunch. Mood and appetite were assessed using visual analogue scales. Changes in food hedonics for different taste and nutrient combinations were assessed using a computer task. The results demonstrated that the LAC manipulation did not exert any immediate effects on mood or appetite. However, LAC did have an effect on food hedonics in individuals with high-trait anxiety after acute stress. These individuals expressed a lower liking (P = 0·012) and SW food preference (P = 0·014) after the stressful task when supplemented with LAC.

Birthing practices of traditional birth attendants in south Asia in the context of training programmes

Traditional Birth Attendants (TBA) training has been an important component of public health policy interventions to improve maternal and child health in developing countries since the 1970s. More recently, since the 1990s, the TBA training strategy has been increasingly seen as irrelevant, ineffective or, on the whole, a failure due to evidence that the maternal mortality rate (MMR) in developing countries had not reduced. Although, worldwide data show that, by choice or out of necessity, 47 percent of births in the developing world are assisted by TBAs and/or family members, funding for TBA training has been reduced and moved to providing skilled birth attendants for all births. Any shift in policy needs to be supported by appropriate evidence on TBA roles in providing maternal and infant health care service and effectiveness of the training programmes. This article reviews literature on the characteristics and role of TBAs in South Asia with an emphasis on India. The aim was to assess the contribution of TBAs in providing maternal and infant health care service at different stages of pregnancy and after-delivery and birthing practices adopted in home births. The review of role revealed that apart from TBAs, there are various other people in the community also involved in making decisions about the welfare and health of the birthing mother and new born baby. However, TBAs have changing, localised but nonetheless significant roles in delivery, postnatal and infant care in India. Certain traditional birthing practices such as bathing babies immediately after birth, not weighing babies after birth and not feeding with colostrum are adopted in home births as well as health institutions in India. There is therefore a thin precarious balance between the application of biomedical and traditional knowledge. Customary rituals and perceptions essentially affect practices in home and institutional births and hence training of TBAs need to be implemented in conjunction with community awareness programmes.