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Uncertainty Assessment (uncertainty + assessment)

Distribution by Scientific Domains

Mathematics and Statistics	50%

Selected Abstracts

Bayesian uncertainty assessment in multicompartment deterministic simulation models for environmental risk assessment

ENVIRONMETRICS, Issue 4 2003
Samantha C. Bates
Abstract We use a special case of Bayesian melding to make inference from deterministic models while accounting for uncertainty in the inputs to the model. The method uses all available information, based on both data and expert knowledge, and extends current methods of ,uncertainty analysis' by updating models using available data. We extend the methodology for use with sequential multicompartment models. We present an application of these methods to deterministic models for concentration of polychlorinated biphenyl (PCB) in soil and vegetables. The results are posterior distributions of concentration in soil and vegetables which account for all available evidence and uncertainty. Model uncertainty is not considered. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Living longer with a greater health burden , changes in the burden of disease and injury in the Northern Territory Indigenous population between 1994,1998 and 1999,2003

AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, Issue 2010
Yuejen Zhao
Abstract Objective: To measure changes over time in the burden of disease for Northern Territory (NT) Indigenous and non-Indigenous population. Methods: The numbers, and crude and age-adjusted rates of disability adjusted life years (DALY) were calculated for periods 1994,1998 and 1999,2003. A measure of information bias was developed to adjust for the tendency of years lost to disability (a component of DALY) to increase over time because of increasing data availability. The jackknife method was used for DALY uncertainty assessment. Results: The all-cause DALY rate was stable for the non-Indigenous population, but increased for the Indigenous population. For both populations, the burden of premature death decreased while the burden of disability increased. For the Indigenous population, there were substantial increases in DALY rates for type 2 diabetes, depression, nephritis/nephrosis, suicide and sense organ disorders. Conclusions: The burden of disease for Indigenous people increased over the study periods, with improvement in the burden of fatal outcomes more than offset by substantial increase in the prevalence and severity of non-fatal conditions. Implications: The paradoxical shift of living longer with a greater health burden has not been previously reported for Indigenous Australians, and highlights the critical importance of prevention for sustaining life expectancy improvement and managing escalation of health costs. This study also demonstrated the usefulness of the DALY to monitor population health. [source]

High dimensional model representation for piece-wise continuous function approximation

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 12 2008
Rajib Chowdhury
Abstract High dimensional model representation (HDMR) approximates multivariate functions in such a way that the component functions of the approximation are ordered starting from a constant and gradually approaching to multivariance as we proceed along the terms like first-order, second-order and so on. Until now HDMR applications include construction of a computational model directly from laboratory/field data, creating an efficient fully equivalent operational model to replace an existing time-consuming mathematical model, identification of key model variables, global uncertainty assessments, efficient quantitative risk assessments, etc. In this paper, the potential of HDMR for tackling univariate and multivariate piece-wise continuous functions is explored. Eight numerical examples are presented to illustrate the performance of HDMR for approximating a univariate or a multivariate piece-wise continuous function with an equivalent continuous function. Copyright © 2007 John Wiley & Sons, Ltd. [source]

When Should Epidemiologic Regressions Use Random Coefficients?

BIOMETRICS, Issue 3 2000
Sander Greenland
Summary. Regression models with random coefficients arise naturally in both frequentist and Bayesian approaches to estimation problems. They are becoming widely available in standard computer packages under the headings of generalized linear mixed models, hierarchical models, and multilevel models. I here argue that such models offer a more scientifically defensible framework for epidemiologic analysis than the fixed-effects models now prevalent in epidemiology. The argument invokes an antiparsimony principle attributed to L. J. Savage, which is that models should be rich enough to reflect the complexity of the relations under study. It also invokes the countervailing principle that you cannot estimate anything if you try to estimate everything (often used to justify parsimony). Regression with random coefficients offers a rational compromise between these principles as well as an alternative to analyses based on standard variable-selection algorithms and their attendant distortion of uncertainty assessments. These points are illustrated with an analysis of data on diet, nutrition, and breast cancer. [source]