An Outline of the Bayesian Decision Theory

In this paper we give an outline on the Bayesian Decision Theory.Comment: arXiv admin note: text overlap with arXiv:1409.826

Prediction of Complex Systems Using Grey Models

Complexity is an inherent property of the world known. According to Kolmogoroff Randomness and Complexity are connected. Therefore the description of randomness using stochastical procedures has been widely used. Nevertheless other methods might be used to predict complex systems, such as Grey Models. In this paper the occurrence of extreme water levels along the Dutch north-sea has been investigated using Grey Models. Other applications are possible and have been carried out by the authors, such as identification of damaged elements in reinforced concrete structural elements

Extreme precipitation and extreme streamflow in the Dongjiang River Basin in southern China

International audienceExtreme hydro-meteorological events have become the focus of more and more studies in the last decade. Due to the complexity of the spatial pattern of changes in precipitation processes, it is still hard to establish a clear view of how precipitation has changed and how it will change in the future. In the present study, changes in extreme precipitation and streamflow processes in the Dongjiang River Basin in southern China are investigated. It was shown that little change is observed in annual extreme precipitation in terms of various indices, but some significant changes are found in the precipitation processes on a monthly basis. The result indicates that when detecting climate changes, besides annual indices, seasonal variations in extreme events should be considered as well. Despite of little change in annual extreme precipitation series, significant changes are detected in several annual extreme flood flow and low-flow series, mainly at the stations along the main channel of Dongjiang River, which are affected significantly by the operation of several major reservoirs. The result highlights the importance of evaluating the impacts of human activities in assessing the changes of extreme streamflows. In addition, three non-parametric methods that are not-commonly used by hydro-meteorology community, i.e., Kolmogorov?Smirnov test, Levene's test and quantile test, are introduced and assessed by Monte Carlo simulation in the present study to test for changes in the distribution, variance and the shift of tails of different groups of dataset. Monte Carlo simulation result shows that, while all three methods work well for detecting changes in two groups of data with large data size (e.g., over 200 points in each group) and big difference in distribution parameters (e.g., over 100% increase of scale parameter in Gamma distribution), none of them are powerful enough for small data sets (e.g., less than 100 points) and small distribution parameter difference (e.g., 50% increase of scale parameter in Gamma distribution)

Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community

Statistical models for quantifying diagnostic accuracy with multiple lesions per patient

We propose random-effects models to summarize and quantify the accuracy of the diagnosis of multiple lesions on a single image without assuming independence between lesions. The number of false-positive lesions was assumed to be distributed as a Poisson mixture, and the proportion of true-positive lesions was assumed to be distributed as a binomial mixture. We considered univariate and bivariate, both parametric and nonparametric mixture models. We applied our tools to simulated data and data of a study assessing diagnostic accuracy of virtual colonography with computed tomography in 200 patients suspected of having one or more polyps

Linking Ground, Space and Knowledge: The Role of Weather Forecasting in Pastoralists\u27 Decision-Making

Changing weather patterns and decreasing land availability continue to challenge the livelihood of the pastoralists in northern Tanzania. The increasing variability of expected rains has complicated livestock management, often jeopardizing household resilience. Drought Early Warning Systems are being set up to contribute to decision-making processes at national and international levels. Nevertheless, due to the large spatial- and temporal resolution of these systems and their high uncertainties, these systems have limited value at a pastoral household level. Therefore, this paper explores what type of weather and climate information is deemed valuable for pastoral households in Longido District, Tanzania. It is based on an ethnographic study, conducted over a period of four months. It explores what weather information would be useful, the necessary scale of desired information, the required lead time of communication and, lastly, the most effective method of communicating forecast information. Following on this data, the study assessed the status of remote sensing and weather forecast modelling, exploring the question, the desired weather information can be forecast with enough skill and at a scale that is relevant to pastoral households in Longido? The ECMWF weather model was used in the assessment, revealing some optimism and scepticism concerning the status of existing information and technologies. Technological recommendations include verification of rainfall data, further research on the rainfall threshold concept, and exploring the model skill of embedded models in Tanzania. At the level of implementation , recommendations include discussing the adverse impacts of actions taken based on the forecasts and forming an implementation advisory group, which includes a comprehensive breadth of stakeholders, such as knowledgeable community members, village leaders, traditional leaders and also professionals from the field of climate sciences, rangeland ecology and anthropology