Robust optimization and portfolio selection: The cost of robustness

Robust optimization is a tractable alternative to stochastic programming particularly suited for problems in which parameter values are unknown, variable and their distributions are uncertain. We evaluate the cost of robustness for the robust counterpart to the maximum return portfolio optimization problem. The uncertainty of asset returns is modelled by polyhedral uncertainty sets as opposed to the earlier proposed ellipsoidal sets. We derive the robust model from a min-regret perspective and examine the properties of robust models with respect to portfolio composition. We investigate the effect of different definitions of the bounds on the uncertainty sets and show that robust models yield well diversified portfolios, in terms of the number of assets and asset weights.     

Validation of thin layer and high performance thin layer chromatographic methods

Analytical validation is a key requirement to asses and to prove a method's reliability and suitability for an intended use. Planar chromatographic procedures are used in different applications ranging from simple screening tests to sophisticated instrumental quantitative assays of analytes in complex matrices. This paper intends to give guidance on how to adopt international accepted formal requirements and guidelines for validation of these different TLC/HPTLC procedures. In addition, some selected parameters for robustness testing and for on going quality assurance of analytical performance based on control charts are reported.     

A Study of Hyperbolicity of Kinetic Stochastic Galerkin System for the Isentropic Euler Equations with Uncertainty

The authors study the fluid dynamic behavior of the stochastic Galerkin (SG for short) approximation to the kinetic Fokker-Planck equation with random uncertainty. While the SG system at the kinetic level is hyperbolic, its fluid dynamic limit, as the Knudsen number goes to zero and the underlying kinetic equation approaches to the uncertain isentropic Euler equations, is not necessarily hyperbolic, as will be shown in the case study fashion for various orders of the SG approximations.     

动态水声环境不确定性的预报与估计

水声环境具有强烈的时空易变性,为了解其变化中的规律和产生的不确定性,将POM海洋数值模型和水声传播模型FOR3D进行耦合,建立海洋—声学耦合数值模式。对海区的温盐等环境参数和水下声场进行预报,给出了典型断面的温度垂直结构、实验海区声速剖面和传播损失。同时,采用集合卡尔曼滤波(EnKF)方法结合海洋—声学耦合数值模式分析了水声环境的不确定性,对不同深度与频率下,传播损失的均值与满足90%的可能区间进行了计算,同时给出了不同点声速、传播损失、声纳作用距离的不确定性直方图。和实验数据的对比结果表明该方法具有较高的有效性,能对水声环境的不确定性进行合理的预报与估计。     

Uncertainty principle and quantum Fisher information

A family of inequalities, related to the uncertainty principle, has been recently proved by S. Luo, Z. Zhang, Q. Zhang, H. Kosaki, K. Yanagi, S. Furuichi and K. Kuriyama. We show that the inequalities have a geometric interpretation in terms of quantum Fisher information. Using this formulation one may naturally ask if this family of inequalities can be further extendend, for example to the RLD quantum Fisher information. We show that this is impossible by producing a family of counterexamples.     

有预算限制的最大多种物资流问题

研究有预算限制的最大多种物资流问题,给出了这个问题的不依赖物资数k的全多项式时间近似算法,其算法复杂性是O~(-ε2m2).同时,利用有预算限制的最大多种物资流问题的研究结果,我们也得到了费用最小的最大多种物资流问题的近似算法和算法复杂性.     

Evaluation of sample processing and sub-sampling performance

A methodology for the estimation of sample processing and sub-sampling performance based on the comparison of the global method experimental dispersion of results with the uncertainty estimated from developed models for the subsequent analytical steps is presented. This approach is a valuable alternative to the evaluation of adequate experimental information using a classical ANOVA, since the significance of the sample processing and sub-sampling is evaluated with a higher number of degrees of freedom for the same number of experimental assays, due to the high number of degrees of freedom associated with the uncertainty estimated for the subsequent analytical steps from the combination of the involved sources of uncertainty.Considering the construction of a model to describe the performance of the analytical steps following sample processing and sub-sampling over a broad concentration range, the experimental assays involved at the evaluation of the sample processing and sub-sampling can be performed at any concentration meeting the previously validated range and several months after the development of that model once its adequacy has been proven over time.This approach, which also allows the construction of a detailed performance model for the global analytical method over a broad concentration range, was applied to the determination of pesticide residues in apples by gas-chromatography with electron capture detector.Considering that no information was available regarding the samples heterogeneity, sub-sampling performance was evaluated considering a sample representing the worst expectable homogeneity. This was accomplished by spiking just one out of the 10 halves of apples processed in each sample.The developed model for the performance of the analytical method was successfully and easily applied to routine analysis through an automated link between the information generated by the chromatograph software with a file containing the model.     

Traceability of measurement results of the effective acquisition time in gamma-ray spectrometry implemented by the pulser method

Measurement of the effective acquisition time of a spectrum by the pulser method is described. The measurement results were verified up to count rates of 12000 s^–1 at various settings of the pulse processing electronics and spectral shapes. Systematic effects of up to 2% were observed. The clocks in the spectrometers were calibrated by counting pulses generated by the DCF 77 signal with the frequency of 1 Hz. In 1-day measurements at low count rates a relative uncertainty of about 0.5 10^–3 of the effective acquisition time was obtained. At counting losses up to 30% the relative uncertainty remained below 1.5 10^–2. Received: 2 March 2002 Accepted: 26 July 2002 Correspondence to M. Korun     

Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model

Analysis of uncertainty is often neglected in the evaluation of complex systems models, such as computational models used in hydrology or ecology. Prediction uncertainty arises from a variety of sources, such as input error, calibration accuracy, parameter sensitivity and parameter uncertainty. In this study, various computational approaches were investigated for analysing the impact of parameter uncertainty on predictions of streamflow for a water-balance hydrological model used in eastern Australia. The parameters and associated equations which had greatest impact on model output were determined by combining differential error analysis and Monte Carlo simulation with stochastic and deterministic sensitivity analysis. This integrated approach aids in the identification of insignificant or redundant parameters and provides support for further simplifications in the mathematical structure underlying the model. Parameter uncertainty was represented by a probability distribution and simulation experiments revealed that the shape (skewness) of the distribution had a significant effect on model output uncertainty. More specifically, increasing negative skewness of the parameter distribution correlated with decreasing width of the model output confidence interval (i.e. resulting in less uncertainty). For skewed distributions, characterisation of uncertainty is more accurate using the confidence interval from the cumulative distribution rather than using variance. The analytic approach also identified the key parameters and the non-linear flux equation most influential in affecting model output uncertainty.     

测量的误差和不确定度

本给出了测量的误差和不确定度的概念，并阐述了它们之间的联系和区别。