Convergence of non-linear functionals of smoothed empirical processes and kernel density estimates

We consider regularizations by convolution of the empirical process and study the asymptotic behaviour of non-linear functionals of this process. Using a result for the same type of non-linear functionals of the Brownian bridge, shown in a previous paper [4], and a strong approximation theorem, we prove several results for the p-deviation in estimation of the derivatives of the density. We also study the asymptotic behaviour of the number of crossings of the smoothed empirical process defined by Yukich [17] and of a modified version of the Kullback deviation.     

Tail-Adaptive Location Rank Test for the Generalized Secant Hyperbolic Distribution

The generalized secant hyperbolic distribution (GSHD) was recently introduced as a modeling tool in data analysis. The GSHD is a unimodal distribution that is completely specified by location, scale, and shape parameters. It has also been shown elsewhere that the rank procedures of location are regular, robust, and asymptotically fully efficient. In this article, we study certain tail weight measures for the GSHD and introduce a tail-adaptive rank procedure of location based on those tail weight measures. We investigate the properties of the new adaptive rank procedure and compare it to some conventional estimators.     

Learning to Manage the Multirisk World

Risk assessment, perception, and management tend to focus on one risk at a time. But we live in a multirisk world. This essay in honor of the 40th anniversary of the Society for Risk Analysis (SRA) and the journal Risk Analysis suggests that we can—and have already begun to—strengthen risk analysis and policy outcomes by moving from a focus on the single to the multiple—multiple stressors, multiple impacts, and multiple decisions. This evolution can improve our abilities to assess actual risks, to confront and weigh risk-risk trade-offs and innovate risk-superior moves, and to build learning into adaptive regulation that adjusts over time. Recognizing the multirisk reality can help us understand complex systems, foresee unintended consequences, design better policy solutions, and learn to improve.     

Toward General Principles for Resilience Engineering

Maintaining the performance of infrastructure-dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first-order system qualities remain elusive in the field. An integrative understanding of how such conditions co-occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure-dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self-organizing nature of infrastructure-dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social-ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience-enhancing principles for infrastructure-dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co-occur and their interdependencies.     

Analysis of nonlinear state space model with dependent measurement noises

This paper presents a nonlinear state space model with considering a first-order autoregressive model for measurement noises. A recursive method using Taylor series based approximations for filtering, prediction and smoothing problem of hidden states from the noisy observations is designed. Also, an expectation-maximization algorithm for calculating the maximum likelihood estimators of parameters is presented. The closed form solutions are obtained for estimating of the hidden states and the unknown parameters. Finally, the performance of the designed methods are verified in a simulation study.     

Discreteness Causes Bias in Percentage-Based Comparisons: A Case Study From Educational Testing

Discretizing continuous distributions can lead to bias in parameter estimates. We present a case study from educational testing that illustrates dramatic consequences of discreteness when discretizing partitions differ across distributions. The percentage of test takers who score above a certain cutoff score (percent above cutoff, or “PAC”) often describes overall performance on a test. Year-over-year changes in PAC, or ΔPAC, have gained prominence under recent U.S. education policies, with public schools facing sanctions if they fail to meet PAC targets. In this article, we describe how test score distributions act as continuous distributions that are discretized inconsistently over time. We show that this can propagate considerable bias to PAC trends, where positive ΔPACs appear negative, and vice versa, for a substantial number of actual tests. A simple model shows that this bias applies to any comparison of PAC statistics in which values for one distribution are discretized differently from values for the other.     

Decision Making in a Standby Service System*

A standby service option allows a firm to lower its risk of not having sufficient capacity to satisfy demand without investing in additional capacity. Standby service options currently exist in the natural gas, electric, and water utility industries. Firms seeking standby service are typically large industrial or institutional organizations that, due to unexpectedly high demand or interruptions in their own supply system, look to a public utility to supplement their requirements. Typically, the firm pays the utility a reservation fee based on a nominated volume and a consumption charge based on the volume actually taken. In this paper, a single‐period model is developed and optimized with respect to the amount of standby capacity a firm should reserve. Expressions for the mean and variance of the supplier's aggregate standby demand distribution are developed. A procedure for computing the level of capacity needed to safely meet its standby obligations is presented. Numerical results suggest that the standby supplier can safely meet its standby demand with a capacity that is generally between 20 to 50% of the aggregate nominated volume.     

汇率机制改革后人民币汇率的动态变化

 汇改后人民币汇率一直处于一个升值趋势,但是2008年后至今将近一年的时间内却基本保持不变。本文利用2005年7月21-2009年8月13日汇率数据探讨人民币篮子中货币权重的动态变化,来解释汇改以来人民币/美元走势。我们发现：一、在整个估计区间内,美元平均权重达到88%,同时美元权重存在结构性变化;二、利用滚动回归(rolling regression),美元权重可以分为三个阶段：2006年10月-2008年8月,2008年9月-2009年1月和2009年2月－2009年8月,分别处于缓慢上升、快速下降和重新回调的趋势中;第三、引入外汇市场压力(EMP),使用月度数据对篮子货币重新进行了考察,欧元在篮子中权重大幅上升,同时汇率对外汇市场压力反应并不明显。因此人民币仍取决于篮子中最主要的两种货币：美元和欧元的权重转换及二种货币间的价值变化。     

地区与国家GDP核算总量数据衔接方法比较研究

 本文以国家数据为准,通过采用2005-2009年的数据为样本,从理论与实证上比较分析了地区与国家GDP数据衔接的三种方法即Geary和Stark的产出估算方法、线性调整法与辅助回归法,比较结果显示：（1）从理论上分析,三种方法都有其合理性,只是辅助回归法较另两种方法更具可取性。（2）从衔接效果上看,辅助回归法优于Geary和Stark的产出估算方法,Geary和Stark的产出估算方法又优于线性调整法。不过不同的方法皆有相应的适用场合与特点以及不同的衔接效果,因而只能说三种方法中有趋优的方法,但不能明确断定何种方法可以具体应用于实际数据衔接中并能达到良好的调整效果。