Risk premiums and benefit measures for generalized-expected-utility theories

Standard tools for the analysis of economic problems involving uncertainty, including risk premiums, certainty equivalents and the notions of absolute and relative risk aversion, are developed without making specific assumptions on functional form beyond the basic requirements of monotonicity, transitivity, continuity, and the presumption that individuals prefer certainty to risk. Individuals are not required to display probabilistic sophistication. The approach relies on the distance and benefit functions to characterize preferences relative to a given state-contingent vector of outcomes. The distance and benefit functions are used to derive absolute and relative risk premiums and to characterize preferences exhibiting constant absolute risk aversion (CARA) and constant relative risk aversion (CRRA). A generalization of the notion of Schur-concavity is presented. If preferences are generalized Schur concave, the absolute and relative risk premiums are generalized Schur convex, and the certainty equivalents are generalized Schur concave.

Size of the first spring generation of Helicoverpa punctigera (Wallengren) (Lepidoptera : Noctuidae) and winter rain in central Australia

Serious infestations of Helicoverpa punctigera are experienced yearly in the eastern cropping regions of Australia. Regression analysis was used to determine whether the size of the first generation in spring (G(1)), which is comprised mostly of immigrants from inland Australia, was related to monthly rainfall in inland winter breeding areas. Data from two long series of light-trap catches at Narrabri in New South Wales (NSW) and Turretfield in South Australia (SA) were used in the analyses. The size of G1 at Narrabri in each year was significantly regressed on the amount of rainfall in western Queensland and NSW in May and June. The size of G1 at Turretfield each year was significantly regressed on the amount of rain in May, June and July in western Queensland and NSW and also in the desert of central Western Australia. Low r(2) values of the regressions suggest that rainfall data for more sites, as well as biological and other physical factors, such as temperature, evaporation, and prevailing wind systems, may need to be included to improve forecasts of the potential magnitude of the infestations in coastal cropping regions.

El Nino and arboviral disease prediction

Recent El Nino events have stimulated interest in the development of modeling techniques to forecast extremes of climate and related health events. Previous studies have documented associations between specific climate variables (particularly temperature and rainfall) and outbreaks of arboviral disease. In some countries, such diseases are sensitive to Fl Nino. Here we describe a climate-based model for the prediction of Ross River virus epidemics in Australia. From a literature search and data on case notifications, we determined in which years there were epidemics of Ross River virus in southern Australia between 1928 and 1998. Predictor variables were monthly Southern Oscillation index values for the year of an epidemic or lagged by 1 year. We found that in southeastern states, epidemic years were well predicted by monthly Southern Oscillation index values in January and September in the previous year. The model forecasts that there is a high probability of epidemic Ross River virus in the southern states of Australia in 1999. We conclude that epidemics of arboviral disease can, at least in principle, be predicted on the basis of climate relationships.

Analysis of long-term light-trap data for Helicoverpa spp. (Lepidoptera : Noctuidae) in Australia: the effect of climate and crop host plants

Regression analyses of a long series of light-trap catches at Narrabri, Australia, were used to describe the seasonal dynamics of Helicoverpa armigera (Hubner). The size of the second generation was significantly related to the size of the first generation, to winter rainfall, which had a positive effect, and to spring rainfall which had a negative effect. These variables accounted for up to 96% of the variation in size of the second generation from year to year. Rainfall and crop hosts were also important for the size of the third generation. The area and tonnage of many potential host crops were significantly correlated with winter rain. When winter rain was omitted from the analysis, the sizes of both the second and third generations could be expressed as a function of the size of the previous generation and of the areas planted to lucerne, sorghum and maize. Lucerne and maize always had positive coefficients and sorghum a negative one. We extended our analysis to catches of H. punctigera (Wallengren), which declines in abundance after the second generation. Winter rain had a positive effect on the sizes of the second and third generations, and rain in spring or early summer had a negative effect. Only the area grown to lucerne had a positive effect on abundance. Forecasts of pest levels from a few months to a few weeks in advance are discussed, along with the improved understanding of the seasonal dynamics of both species and the significance of crops in the management of insecticide resistance for H. armigera.

Minimum-order stable recursive filter design via the genetic algorithm approach

In this paper, the minimum-order stable recursive filter design problem is proposed and investigated. This problem is playing an important role in pipeline implementation sin signal processing. Here, the existence of a high-order stable recursive filter is proved theoretically, in which the upper bound for the highest order of stable filters is given. Then the minimum-order stable linear predictor is obtained via solving an optimization problem. In this paper, the popular genetic algorithm approach is adopted since it is a heuristic probabilistic optimization technique and has been widely used in engineering designs. Finally, an illustrative example is sued to show the effectiveness of the proposed algorithm.

A landscape-scale test of the predictive ability of a spatially explicit model for population viability analysis

1. Although population viability analysis (PVA) is widely employed, forecasts from PVA models are rarely tested. This study in a fragmented forest in southern Australia contrasted field data on patch occupancy and abundance for the arboreal marsupial greater glider Petauroides volans with predictions from a generic spatially explicit PVA model. This work represents one of the first landscape-scale tests of its type. 2. Initially we contrasted field data from a set of eucalypt forest patches totalling 437 ha with a naive null model in which forecasts of patch occupancy were made, assuming no fragmentation effects and based simply on remnant area and measured densities derived from nearby unfragmented forest. The naive null model predicted an average total of approximately 170 greater gliders, considerably greater than the true count (n = 81). 3. Congruence was examined between field data and predictions from PVA under several metapopulation modelling scenarios. The metapopulation models performed better than the naive null model. Logistic regression showed highly significant positive relationships between predicted and actual patch occupancy for the four scenarios (P = 0.001-0.006). When the model-derived probability of patch occupancy was high (0.50-0.75, 0.75-1.00), there was greater congruence between actual patch occupancy and the predicted probability of occupancy. 4. For many patches, probability distribution functions indicated that model predictions for animal abundance in a given patch were not outside those expected by chance. However, for some patches the model either substantially over-predicted or under-predicted actual abundance. Some important processes, such as inter-patch dispersal, that influence the distribution and abundance of the greater glider may not have been adequately modelled. 5. Additional landscape-scale tests of PVA models, on a wider range of species, are required to assess further predictions made using these tools. This will help determine those taxa for which predictions are and are not accurate and give insights for improving models for applied conservation management.

Does management accounting play role in planning process?

This study examines the relationship between management accounting and planning profiles in Brazilian companies. The main goal is to understand the consequences of not including a fully structured management accounting scheme in the planning process. The authors conducted a field research among medium and large-sized companies, using a probabilistic sample from a population of 2281 companies. Using analytic hierarchy process (AHP) and statistical cluster analysis, the authors grouped the entities` strategic budget planning processes into five profiles, after which the authors applied statistical tests to assess the five clusters. The study concludes that poor or fully implemented strategic and budget-planning processes relate to the management accounting profiles of the Brazilian organizations studied. (C) 2009 Elsevier Inc. All rights reserved.

Assessing cost-effectiveness in mental health: family interventions for schizophrenia and related conditions

Objective: Existing evidence suggests that family interventions can be effective in reducing relapse rates in schizophrenia and related conditions. Despite this, such interventions are not routinely delivered in Australian mental health services. The objective of the current study is to investigate the incremental cost-effectiveness ratios (ICERs) of introducing three types of family interventions, namely: behavioural family management (BFM); behavioural intervention for families (BIF); and multiple family groups (MFG) into current mental health services in Australia. Method: The ICER of each of the family interventions is assessed from a health sector perspective, including the government, persons with schizophrenia and their families/carers using a standardized methodology. A two-stage approach is taken to the assessment of benefit. The first stage involves a quantitative analysis based on disability-adjusted life years (DALYs) averted. The second stage involves application of 'second filter' criteria (including equity, strength of evidence, feasibility and acceptability to stakeholders) to results. The robustness of results is tested using multivariate probabilistic sensitivity analysis. Results: The most cost-effective intervention, in order of magnitude, is BIF (A$8000 per DALY averted), followed by MFG (A$21 000 per DALY averted) and lastly BFM (A$28 000 per DALY averted). The inclusion of time costs makes BFM more cost-effective than MFG. Variation of discount rate has no effect on conclusions. Conclusions: All three interventions are considered 'value-for-money' within an Australian context. This conclusion needs to be tempered against the methodological challenge of converting clinical outcomes into a generic economic outcome measure (DALY). Issues surrounding the feasibility of routinely implementing such interventions need to be addressed.

Comparing predictions of extinction risk using models and subjective judgement

Models of population dynamics are commonly used to predict risks in ecology, particularly risks of population decline. There is often considerable uncertainty associated with these predictions. However, alternatives to predictions based on population models have not been assessed. We used simulation models of hypothetical species to generate the kinds of data that might typically be available to ecologists and then invited other researchers to predict risks of population declines using these data. The accuracy of the predictions was assessed by comparison with the forecasts of the original model. The researchers used either population models or subjective judgement to make their predictions. Predictions made using models were only slightly more accurate than subjective judgements of risk. However, predictions using models tended to be unbiased, while subjective judgements were biased towards over-estimation. Psychology literature suggests that the bias of subjective judgements is likely to vary somewhat unpredictably among people, depending on their stake in the outcome. This will make subjective predictions more uncertain and less transparent than those based on models. (C) 2004 Elsevier SAS. All rights reserved.

Numerical simulation of dendrite growth during solidification

A comprehensive probabilistic model for simulating dendrite morphology and investigating dendritic growth kinetics during solidification has been developed, based on a modified Cellular Automaton (mCA) for microscopic modeling of nucleation, growth of crystals and solute diffusion. The mCA model numerically calculated solute redistribution both in the solid and liquid phases, the curvature of dendrite tips and the growth anisotropy. This modeling takes account of thermal, curvature and solute diffusion effects. Therefore, it can simulate microstructure formation both on the scale of the dendrite tip length. This model was then applied for simulating dendritic solidification of an Al-7%Si alloy. Both directional and equiaxed dendritic growth has been performed to investigate the growth anisotropy and cooling rate on dendrite morphology. Furthermore, the competitive growth and selection of dendritic crystals have also investigated.

Breast cancer five-year survival in New South Wales women, 1972 to 1991

This study of breast cancer survival is based on analysis of five-year relative survival of 38 362 cases of invasive breast cancer in New South Wales (NSW) women, incident between 1972 and 1991, with follow-up to 1992, using data from the population-based NSW Central Cancer Registry. Survival was ascertained by matching the registry file of breast cancers against NSW death certificates from 1972 to 1992, mainly by automated probabilistic linkage. Absolute survival of cases was compared with expected survival of age- and period-matched NSW women. Proportional hazard regression analysis was used for examination of the effects on excess mortality of age, period of diagnosis and degree of spread at diagnosis. Relative survival at five years increased from 70 per cent in 1972-1976 to 77 per cent in 1987-1991. Survival improved during the 1970s and in the late 1980s. Regression analysis suggested that part of the improved survival in the late 1980s was due to lesser degree of spread at diagnosis, whereas the improved survival during the 1970s may have been due to treatment. Survival was better for those aged 40-49 years (RR = 0.86) and worse for those aged greater than or equal to 70 years (RR = 1.22) compared with the referent group (60-69 years). Excess mortality was much less for those with invasive localised disease than those with regional spread (RR = 3.1) or metastatic cancer (RR = 15.5) at diagnosis. For the most recent period (1987-1991), relative five-year survival was 90, 70 and 18 per cent, respectively, for the three degree-of-spread categories.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.

Assessment of factors that confound MRI and neuropathological correlation of human postmortem brain tissue

In spite of considerable technical advance in MRI techniques, the optical resolution of these methods are still limited. Consequently, the delineation of cytoarchitectonic fields based on probabilistic maps and brain volume changes, as well as small-scale changes seen in MRI scans need to be verified by neuronanatomical/neuropathological diagnostic tools. To attend the current interdisciplinary needs of the scientific community, brain banks have to broaden their scope in order to provide high quality tissue suitable for neuroimaging- neuropathology/anatomy correlation studies. The Brain Bank of the Brazilian Aging Brain Research Group (BBBABSG) of the University of Sao Paulo Medical School (USPMS) collaborates with researchers interested in neuroimaging-neuropathological correlation studies providing brains submitted to postmortem MRI in-situ. In this paper we describe and discuss the parameters established by the BBBABSG to select and to handle brains for fine-scale neuroimaging-neuropathological correlation studies, and to exclude inappropriate/unsuitable autopsy brains. We tried to assess the impact of the postmortem time and storage of the corpse on the quality of the MRI scans and to establish fixation protocols that are the most appropriate to these correlation studies. After investigation of a total of 36 brains, postmortem interval and low body temperature proved to be the main factors determining the quality of routine MRI protocols. Perfusion fixation of the brains after autopsy by mannitol 20% followed by formalin 20% was the best method for preserving the original brain shape and volume, and for allowing further routine and immunohistochemical staining. Taken to together, these parameters offer a methodological progress in screening and processing of human postmortem tissue in order to guarantee high quality material for unbiased correlation studies and to avoid expenditures by post-imaging analyses and histological processing of brain tissue.

Prevalence of cannabis use in Brazil: Data from the I Brazilian National Alcohol Survey (BNAS)

Aims: To estimate the prevalence of cannabis use in the last 12 months in the Brazilian population and to examine its association with individual and geographic characteristics. Design: Cross-sectional survey with a national probabilistic sample. Participants: 3006 individuals aged 14 to 65 years. Measurements: Questionnaire based on well established instruments, adapted to the Brazilian population. Findings: The 12-month prevalence of cannabis use was 2.1% (95%Cl 1.3-2.9). Male gender, better educational level, unemployment and living in the regions South and Southeast were independently associated with higher 12-month prevalence of cannabis use. Conclusion: While the prevalence of cannabis use in Brazil is lower than in many countries, the profile of those who are more likely to have used it is similar. Educational and prevention policies should be focused on specific population groups. (C) 2009 Elsevier Ltd. All rights reserved.

A practical approach to assess depression risk and to guide risk reduction strategies in later life

Background: Many factors have been associated with the onset and maintenance of depressive symptoms in later life, although this knowledge is yet to be translated into significant health gains for the population. This study gathered information about common modifiable and non-modifiable risk factors for depression with the aim of developing a practical probabilistic model of depression that can be used to guide risk reduction strategies. \Methods: A cross-sectional study was undertaken of 20,677 community-dwelling Australians aged 60 years or over in contact with their general practitioner during the preceding 12 months. Prevalent depression (minor or major) according to the Patient Health Questionnaire (PHQ-9) assessment was the main outcome of interest. Other measured exposures included self-reported age, gender, education, loss of mother or father before age 15 years, physical or sexual abuse before age 15 years, marital status, financial stress, social support, smoking and alcohol use, physical activity, obesity, diabetes, hypertension, and prevalent cardiovascular diseases, chronic respiratory diseases and cancer. Results: The mean age of participants was 71.7 +/- 7.6 years and 57.9% were women. Depression was present in 1665 (8.0%) of our subjects. Multivariate logistic regression showed depression was independently associated with age older than 75 years, childhood adverse experiences, adverse lifestyle practices (smoking, risk alcohol use, physical inactivity), intermediate health hazards (obesity, diabetes and hypertension), comorbid medical conditions (clinical history of coronary heart disease, stroke, asthma, chronic obstructive pulmonary disease, emphysema or cancers), and social or financial strain. We stratified the exposures to build a matrix that showed that the probability of depression increased progressively with the accumulation of risk factors, from less than 3% for those with no adverse factors to more than 80% for people reporting the maximum number of risk factors. Conclusions: Our probabilistic matrix can be used to estimate depression risk and to guide the introduction of risk reduction strategies. Future studies should now aim to clarify whether interventions designed to mitigate the impact of risk factors can change the prevalence and incidence of depression in later life.