Backtesting portfolio value-at-risk with estimated portfolio weights

This article theoretically and empirically analyzes backtesting portfolio value-at-risk (VaR) with estimation risk in an intrinsically multi-variate framework. It particularly takes into account the estimation of portfolio weights in forecasting portfolio VaR and its impact on backtesting. It shows that the estimation risk from estimating portfolio weights and that from estimating the multi-variate dynamic model make the existing methods in a univariate framework inapplicable. It proposes a general theory to quantify estimation risk applicable to the present problem and suggests practitioners a simple but effective way to implement valid inference to overcome the effect of estimation risk in backtesting portfolio VaR. In particular, we apply our theory to the efficient mean-variance-skewness portfolio for a multi-variate generalized autoregressive conditional heteroscedasticity model with multi-variate general hyperbolic distributed innovations. Some Monte Carlo simulations and an empirical application demonstrate the merits of our method.     

A resilient buffer allocation scheme in active queue management: a stochastic cooperative game theoretic approach

During the last decade, a plentiful number of active queue management schemes have been proposed, but their main objectives are simply allocating the buffer resource to all flows evenly, or protecting responsive flows from being degraded by unresponsive flows. However, the sending rates of the responsive flows can be determined diversely, and not all unresponsive flows have aggressively high sending rates. Furthermore, it is rational to reserve a portion of the buffer resource for certain privileged traffic. Grounded by these evidences, in this paper, we present a resilient active queue management algorithm, named Prior‐Core‐based Buffer Allocation considering diverse congestion control algorithms, fair‐unresponsive flows, and some privileged traffic. Our approach is based on stochastic cooperative game theory, where the payoffs yielded by cooperation are described by random variables, and the core is defined only over the distribution of these random payoffs; the core in this situation is called the prior‐core. As a result, it is shown that our buffer allocation, yielded by the prior‐core, achieves completely fair allocation for those flows whose requirement does not exceed the fair‐share regardless of the responsiveness, whereas aggressive flows are restricted according to availability of the buffer; all these are verified through ns‐2 simulation experiments. Copyright © 2014 John Wiley & Sons, Ltd.     

A distributed state estimation method for power systems incorporating linear and nonlinear models

High-voltage transmission networks are commonly equipped with phasor measurement units (PMU), and some of them are PMU observable. However, PMUs are seldom installed in distribution networks due to budget limitations. The state estimation equations of PMU observable areas are linear, while those of other areas remain nonlinear. This paper proposes a new distributed state estimation method for solving multi-area state estimation problems, in which linear models are used for high-voltage transmission network, while nonlinear models are adopted for other areas. In PMU observable area, we select coordinating variables as generalized cost functions which accurately represent the sensitivity between the linear SE objective function and boundary states. Consequently, SE results identical to a centralized estimator can be obtained without iterations at the coordination level. This paper presents the problem model and theoretical analysis of the proposed method, and shows its effectiveness by numerical tests.     

A new class of nonlinear Rauch–Tung–Striebel cubature Kalman smoothers

In this paper, a new Rauch–Tung–Striebel type of nonlinear smoothing method is proposed based on a class of high-degree cubature integration rules. This new class of cubature Kalman smoothers generalizes the conventional third-degree cubature Kalman smoother using the combination of Genz׳s or Mysovskikh׳s high-degree spherical rule with the moment matching based arbitrary-degree radial rule, which considerably improves the estimation accuracy. A target tracking problem is utilized to demonstrate the performance of this new smoother and to compare it with other Gaussian approximation smoothers. It will be shown that this new cubature Kalman smoother enhances the filtering accuracy and outperforms the extended Kalman smoother, the unscented Kalman smoother, and the conventional third-degree cubature Kalman smoother. It also maintains close performance to the Gauss–Hermite quadrature smoother with much less computational cost.     

基于多项式变换的参数估计方法

本文给出一种采用DPT估计SNCK信号时宽—带宽积的方法,并通过仿真该估计方法的性能与其它估计方法进行比较.首先给出SNCK信号参数估计的一般过程.为了便于计算和理论推导,根据估计出的中心频率将接收到的SNCK信号搬移到零频,从而进一步估计其它参数,如采用DPT估计SNCK信号时宽带宽积.本文将重点研究采用DPT算法估计SNCK信号值的方法.     

A pseudo-likelihood approach for estimating diagnostic accuracy of multiple binary medical tests

Latent class models with crossed subject-specific and test(rater)-specific random effects have been proposed to estimate the diagnostic accuracy (sensitivity and specificity) of a group of binary tests or binary ratings. However, the computation of these models are hindered by their complicated Monte Carlo Expectation–Maximization (MCEM) algorithm. In this article, a class of pseudo-likelihood functions is developed for conducting statistical inference with crossed random-effects latent class models in diagnostic medicine. Theoretically, the maximum pseudo-likelihood estimation is still consistent and has asymptotic normality. Numerically, our results show that not only the pseudo-likelihood approach significantly reduces the computational time, but it has comparable efficiency relative to the MCEM algorithm. In addition, dimension-wise likelihood, one of the proposed pseudo-likelihoods, demonstrates its superior performance in estimating sensitivity and specificity.     

A novel method for estimating the track-soil parameters based on Kalman and improved strong tracking filters

A tracked vehicle has been widely used in exploring unknown environments and military fields. In current methods for suiting soil conditions, soil parameters need to be given and the traction performance cannot always be satisfied on soft soil. To solve the problem, it is essential to estimate track-soil parameters in real-time. Therefore, a detailed mathematical model is proposed for the first time. Furthermore, a novel algorithm which is composed of Kalman filter (KF) and improved strong tracking filter (STF) is developed for online track-soil estimation and named as KF–ISTF. By this method, the KF is used to estimate slip parameters, and the ISTF is used to estimate motion states. Then the key soil parameters can be estimated by using a suitable soil model. The experimental results show that equipped with the estimation algorithm, the proposed model can be used to estimate the track-soil parameters, and make the traction performance satisfied with soil conditions.     

A new numerical algorithm to check the observability and bad-data processing ability of the metering scheme

This paper provides a unified approach for the optimization of measurements placements employed for power system online monitoring through state estimation. The proposed methodology, which can be suitable for the mixed measure system, preserves state estimation observability and bad-data processing capability by employing numerical algorithms for observability checking, critical measurements and critical couple identification. First, node injection radix measurements and measurement categories are defined. According to the above definitions, the coefficient matrix can be solved. The analysis on the column vectors of the coefficient matrix can determine each measurement classification. Furthermore, the numbers of each measurement class contains can determine bad-data processing capability. The observability can be checked by the type number of measurements. The proposed method is illustrated with the IEEE39-bus system and the IEEE118-bus system. Results from the case studies are presented to demonstrate that the approach adequately fulfills the desired properties related to observability, bad-data processing, cost, and robustness.     

Interacting force estimation during blade/seal rubs

Abradable seals are used in aeronautical compressors to minimize the blade–casing clearance while ensuring the integrity of the mechanical parts in the invent of contact. Considering the tight clearance, in-flight blade–seal interactions may occur and have to be taken into account when designing abradable seals. The blade–seal interacting force is difficult to access but constitutes a key feature to understand the phenomena and to develop numerical models. The present paper aims to present an experimental methodology to estimate the blade–seal interacting force from indirect measurements. The methodology has been applied to a short interaction produced on a dedicated test rig in conditions representative of the full-scale configuration. The obtained force was correlated with the wear profile of the abradable seal.     

An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle

Vehicle state is essential for active safety stability control. However, the accurate measurement of some vehicle states is difficult to achieve without the use of expensive equipment. To improve estimation accuracy in real time, this paper proposes an estimator of vehicle velocity based on the adaptive unscented Kalman filter (AUKF) for an in‐wheel‐motored electric vehicle (IWMEV). Given the merits of an independent drive structure, the tire forces of the IWMEV can be directly calculated through a vehicle dynamic model. Additionally, by means of the normalized innovation square, the validity of vehicle velocity estimation can be detected, and the sliding window length can be adjusted adaptively; thus, the steady‐state error and the dynamic performance of the IWMEV are demonstrated to be simultaneously improved over an alternative approach in comparisons. Then, an adaptive adjustment strategy for the noise covariance matrices is introduced to overcome the impact of parameter uncertainties. The numerically simulated and experimental results prove that the proposed vehicle velocity estimator based on AUKF not only improves estimation accuracy but also possesses strong robustness against parameter uncertainties. The deployment of the estimation algorithm by using a single‐chip microcomputer verifies the strong real‐time performance and easy‐to‐implement characteristics of the proposed algorithm.