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Abstract Background HALE is now a regular strategic planning indicator for all levels of the Chinese government. However, HALE measurements necessitate comprehensive data collection and intricate technology. Therefore, effectively converting numerous diseases into the years lived with disability (YLD) rate is a significant challenge for HALE measurements. Our study aimed to construct a simple YLD rate measurement model with high applicability based on the current situation of actual data resources within China to address challenges in measuring HALE target values during planning. Methods First, based on the Chinese YLD rate in the Global Burden of Disease (GBD) 2019, Pearson correlation analysis, the global optimum method, etc., was utilized to screen the best predictor variables from the current Chinese data resources. Missing data for predictor variables were filled in via spline interpolation. Then, multiple linear regression models were fitted to construct the YLD rate measurement model. The Sullivan method was used to measure HALE. The Monte Carlo method was employed to generate 95% uncertainty intervals. Finally, model performances were assessed using the mean absolute error (MAE) and mean absolute percentage error (MAPE). Results A three-input-parameter model was constructed to measure the age-specific YLD rates by sex in China, directly using the incidence of infectious diseases, the incidence of chronic diseases among persons aged 15 and older, and the addition of an under-five mortality rate covariate. The total MAE and MAPE for the combined YLD rate were 0.0007 and 0.5949%, respectively. The MAE and MAPE of the combined HALE in the 0-year-old group were 0.0341 and 0.0526%, respectively. There were slightly fewer males (0.0197, 0.0311%) than females (0.0501, 0.0755%). Conclusion We constructed a high-accuracy model to measure the YLD rate in China by using three monitoring indicators from the Chinese national routine as predictor variables. The model provides a realistic and feasible solution for measuring HALE at the national and especially regional levels, considering limited data.

Background: HALE is now a regular strategic planning indicator for all levels of the Chinese government. However, HALE measurements necessitate comprehensive data collection and intricate technology. Therefore, effectively converting numerous diseases into the years lived with disability (YLD) rate is a significant challenge for HALE measurements. Our study aimed to construct a simple YLD rate measurement model with high applicability based on the current situation of actual data resources within China to address challenges in measuring HALE target values during planning. Methods: First, based on the Chinese YLD rate in the Global Burden of Disease (GBD) 2019, Pearson correlation analysis, the global optimum method, etc., was utilized to screen the best predictor variables from the current Chinese data resources. Missing data for predictor variables were filled in via spline interpolation. Then, multiple linear regression models were fitted to construct the YLD rate measurement model. The Sullivan method was used to measure HALE. The Monte Carlo method was employed to generate 95% uncertainty intervals. Finally, model performances were assessed using the mean absolute error (MAE) and mean absolute percentage error (MAPE). Results: A three-input-parameter model was constructed to measure the age-specific YLD rates by sex in China, directly using the incidence of infectious diseases, the incidence of chronic diseases among persons aged 15 and older, and the addition of an under-five mortality rate covariate. The total MAE and MAPE for the combined YLD rate were 0.0007 and 0.5949%, respectively. The MAE and MAPE of the combined HALE in the 0-year-old group were 0.0341 and 0.0526%, respectively. There were slightly fewer males (0.0197, 0.0311%) than females (0.0501, 0.0755%). Conclusion: We constructed a high-accuracy model to measure the YLD rate in China by using three monitoring indicators from the Chinese national routine as predictor variables. The model provides a realistic and feasible solution for measuring HALE at the national and especially regional levels, considering limited data. [ABSTRACT FROM AUTHOR]

This study investigates the best approach to calibrate an event-based conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model, comparing different trials of single-objective, single-event multi-objective (SEMO), and multi-event-multi-objective (MEMO) model calibrations using root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), and Bias as objective functions. Model performance was validated for several peak events via 90% confidence interval (CI)-based output uncertainty quantification of relative error of discharges. Multi-objective optimization yielded more accurate and robust solutions compared to single-objective calibrations. Ensembles of Pareto solutions from the multi-objective calibrations better characterized the flood peaks within the uncertainty intervals. MEMO calibration exhibited lower uncertainties and better prediction of peak events versus SEMO calibration. Moreover, the MEMO_6D (six-dimensional) approach outperformed the SEMO_3D and MEMO_3D in capturing the larger peak events. This study suggests that the MEMO_6D is the best approach for predicting large flood events with lower model output uncertainties when the calibration is performed with a better combination of peak events. [ABSTRACT FROM AUTHOR]

Abstract General noise cost functions have been recently proposed for support vector regression (SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical $$\epsilon$$ ϵ -SVR. On the other hand, uncertainty estimates for SVR have received a somewhat limited attention in the literature until now and still have unaddressed problems. Keeping this in mind, three main goals are addressed here. First, we propose a framework that uses a combination of general noise SVR models with naive online R minimization algorithm (NORMA) as optimization method, and then gives non-constant error intervals dependent upon input data aided by the use of clustering techniques. We give theoretical details required to implement this framework for Laplace, Gaussian, Beta, Weibull and Marshall–Olkin generalized exponential distributions. Second, we test the proposed framework in two real-world regression problems using data of two public competitions about solar energy. Results show the validity of our models and an improvement over classical $$\epsilon$$ ϵ -SVR. Finally, in accordance with the principle of reproducible research, we make sure that data and model implementations used for the experiments are easily and publicly accessible.

Both hydrodynamic factors and social factors have large impacts on the loss of life caused by dam failure. Relatively large uncertainty intervals of the influencing factors lead to changes in the potential loss of life. Based on an analysis of the formation mechanism of loss of life, the influencing factors were identified. Combined with interval theory, a method for calculating loss of life and determining the impacts of the influencing factors on loss of life was proposed. The intervals of the exposure rate of the population at risk and the mortality of the exposed population, which are impacted by the major influencing factors such as the flood severity, warning time, understanding of dam failure, and building vulnerability, were recommended. Furthermore, a range of correction coefficients caused by the minor influencing factors, such as the dam failure time, rescue ability, and age distribution, was analyzed. The proposed method was validated by analyzing the losses of life in 21 flooded regions after 10 dam failure events and 2 flash river floods, in which the intervals of the estimated results all contained the actual loss of life. In addition, the ratios of the upper bounds to the corresponding lower bounds of the intervals were all less than 10, which is in accordance with the characteristic that the results of different existing methods vary within an order of magnitude. This is the first work that pays careful attention to the uncertainty intervals of loss of life estimates, and the proposed method effectively determined the severity of the potential loss of life caused by dam failure. [ABSTRACT FROM AUTHOR]

With the increasing proportion of wind power connected to grid, power system dispatching is facing more and more challenges from uncertainty. To cope with this uncertainty, robust optimization has been applied in unit commitment (UC) problem. In this paper, a multi‐band uncertainty set considering the temporal correlation (MBUSCTC) of wind/load prediction error is proposed firstly, which has two characteristics: (1) The MBUSCTC rigorously and realistically reflect the distribution characteristics of uncertainties in uncertainty intervals, thereby effectively reducing the conservatism of the traditional singe‐band uncertainty set; (2) the temporal correlation constraints of wind power/load prediction errors in MBUSCTC could limit the realization of uncertainties fluctuating frequently in uncertain intervals, thereby eliminating scenarios with lower probability in uncertainty sets. Then the proposed MBUSCTC is applied to UC problem, leading a robust UC model based on MBUSCTC, which is solved by Benders decomposition method and C&CG method. Finally, case studies based on the modified IEEE‐118 bus system and an actual power system of China demonstrate that the proposed method can effectively reduce the conservativeness of the robust UC model and ensure the robustness of the unit commitment solution. [ABSTRACT FROM AUTHOR]

Causal inference with observational data can be performed under an assumption of no unobserved confounders (unconfoundedness assumption). There is, however, seldom clear subject‐matter or empirical evidence for such an assumption. We therefore develop uncertainty intervals for average causal effects based on outcome regression estimators and doubly robust estimators, which provide inference taking into account both sampling variability and uncertainty due to unobserved confounders. In contrast with sampling variation, uncertainty due to unobserved confounding does not decrease with increasing sample size. The intervals introduced are obtained by modeling the treatment assignment mechanism and its correlation with the outcome given the observed confounders, allowing us to derive the bias of the estimators due to unobserved confounders. We are thus also able to contrast the size of the bias due to violation of the unconfoundedness assumption, with bias due to misspecification of the models used to explain potential outcomes. This is illustrated through numerical experiments where bias due to moderate unobserved confounding dominates misspecification bias for typical situations in terms of sample size and modeling assumptions. We also study the empirical coverage of the uncertainty intervals introduced and apply the results to a study of the effect of regular food intake on health. An R‐package implementing the inference proposed is available. [ABSTRACT FROM AUTHOR]

Abstract: Practical applications of life cycle assessment (LCA) to agricultural production systems require articulating uncertainties caused by scenario indeterminacy, because practitioners do not have sufficient knowledge about agricultural input production processes. However, current understanding about scenario uncertainties is still limited on account of insufficient knowledge. We propose a method to quantify scenario uncertainty in agricultural inputs and to assess the uncertainty in comparative LCA of agricultural production systems. We formulate uncertainty intervals due to scenario indeterminacy (a situation in which decision makers face decision problems without determining the details of the scenarios) and derive uncertainty intervals for conventional, environmentally friendly, and organic rice production systems in Japan, in addition to those for chemical fertilizer production and distribution. The result indicates that, although lack of information on transportation scenarios caused uncertainty, the ranking of global warming potential for conventional, environmentally friendly, and organic rice production systems remained unchanged. It implies that the result is stable with respect to scenario uncertainties due to location of fertilizer production. The proposed methodology for understanding the stability of results can be further developed as a technique to deal with uncertainty and instability in LCA of agricultural production systems. [Copyright &y& Elsevier]

The main advances regarding the deep connections between probability and possibility measurement uncertainty representation (but not the propagation) over the last decade are reviewed. They concern the following: the definition of a possibility distribution equivalent to a probability of one from its whole set of dispersion intervals about one point for all of the probability levels, the bridges with the conventional dispersion parameters, the representation of a partial probability knowledge owing to a maximum specificity principle better than the maximum entropy principle, and also probability inequalities. The use of a possibility representation for common measurement situations such as the description of measurement results, measurand estimation, and expression of a priori uncertainty information is illustrated and then discussed in view of their use in further processing (propagation and fuzzy inference systems). The conclusion highlights the interests of the possibility approach and points out some remaining issues. [ABSTRACT FROM PUBLISHER]

The objective of this research is to show that National Bank of Romania follow the international pattern by providing inflation rate forecasts based on its own model better than the target inflation. Starting from quarterly values for the annual inflation, for 2012 the forecasts based on the institution macro-econometric models were more accurate than the annual target fixed for each quarter. The accuracy of inflation targets made for 2013 was evaluated in ex-ante variant, choosing as benchmark forecasts those provided by NBR and the na?ve ones. This study introduces as a novelty in literature some measures of accuracy and it proposes the evaluation of accuracy for uncertainty intervals using only the lower, respectively the upper limit of each forecast interval. Only with some exceptions the errors based on the inferior limit of uncertainty intervals proposed by NBR are smaller than those computed using the superior boundaries as point forecasts. In ex-ante variant, for 2013 the targets for this year and the NBR forecasts based on econometric models were chosen as possible realizations. If the targeted inflation is considered as the real value of inflation in the first two quarters of 2013 the upper limits of intervals are recommended to be chosen unlike the inferior boundaries for the third and the fourth quarters from 2013.This paper is an original research not only for assessing NBR forecasts accuracy, but also for the proposal of new methods of evaluating the accuracy for point forecasts and uncertainty intervals. [ABSTRACT FROM AUTHOR]

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In this paper, a lattice ℓ-step ahead output uncertainty prediction algorithm is presented. The proposed prediction scheme is applicable to linear, stable, auto-regressive (AR) systems. The uncertainty predictors utilize the structure of the lattice filters. Subject to the saturation limits of the excitation signal, the measurement of the system output, and the a priori bounds of the reflection coefficients, the set of the feasible predicted output variables is computed. The suggested scheme is recursively computed over an ℓ-step ahead future time window, while the prediction uncertainty intervals are similarly computed over the lattice stages, regarding the forward and the backward prediction errors. Simulation results are presented that prove the efficacy of the employed lattice uncertainty predictors. [ABSTRACT FROM AUTHOR]

Abstract: The estimation of the Feasible Parameter Set (FPS) for Hammerstein models in a worst-case setting is considered. A bounding procedure is determined both for polytopic and ellipsoidic uncertainties. It consists in the projection of the FPS of the extended parameter vector onto suitable subspaces and in the solution of convex optimization problems which provide Uncertainties Intervals of the model parameters. The bounds obtained are tighter than in the previous approaches. [Copyright &y& Elsevier]

The use of societal data in environmental research has indicated a need for considering uncertainties of data. Several fundamental conditions for statistical treatment are occasionally not met. The choice is either to use or to ignore the uncertain data. If used, it may impair the quality of the result. If ignored, a possible environmental risk may remain unattended. This article discusses some of the problems encountered with data in urban heavy-metal metabolism, and suggests a method based on uncertainty intervals to consider the uncertainties. [ABSTRACT FROM AUTHOR]