Application of bootstrap re-sampling method in statistical measurement of sustainability

Sustainability is of essential interest for many organizations and is defined as the ability to maintain existing resources at a certain rate or level when encountered with barriers. Factors affecting sustainability are categorized as enablers (capacities) and barriers (challenges) that have positive and negative effects on sustainability, respectively. To evaluate the status of sustainability, organizations need a measurement method to account for all the aspects of the sustainability classified into social, economic, and environmental tiers. Previously many researchers have provided indexes and measure specific to the studied field, which is not applicable to other areas. Furthermore, the proposed methods fail to cover all the aspects of sustainability. This study investigates a statistical method to measure the sustainability and the application of the bootstrap re-sampling method in order to overcome the problem with normality assumption when the sample size is not large enough and thus develop a more realistic stochastic model. The Bootstrap re-sampling method enables the unbiased estimation of population parameters such as mean and standard deviation. The proposed method is evaluated by comparing its results with those found in the literature.     

Measuring customer experience in service: A systematic review

The aim of this systematic review is to identify how customer experience in the service sector has been measured in relevant publications in the marketing field. A sample of 33 papers was collected from two electronic databases—the Web of Science (Thomson Reuters) and Scopus (Elsevier)—covering a large number of publications. After analyzing the articles and reviewing the customer experience literature, the following are our main contributions: (i) clarification of the concepts that appear in the literature review of customer experience in the service sector; (ii) classification of the variables, scales, and constructs related to customer experience in service; (iii) demonstration of the service experience as the preponderant construct that is used to measure customer experience in service; and (iv) proposal of a new dimension—the concept of ‘pre-experience’—to measure customer experience in service. These contributions can provide a more solid basis for measuring customer experience in service.     

Can welfare be measured with a preference-satisfaction index?

Welfare in economics is generally conceived of in terms of the satisfaction of preferences, but a general, comparable index measure of welfare is generally not taken to be possible. In recent years, in response to the usage of measures of subjective well-being as indices of welfare in economics, a number of economists have started to develop measures of welfare based on preference-satisfaction. In order to evaluate the success of such measures, I formulate criteria of policy-relevance and theoretical success in the context of preference-satisfaction measures of welfare. I present a detailed case study of the methodological choices put forward in a prominent generalized proposal for measuring welfare through preferences recently published in the American Economic Review. I contrast this with an alternative welfare measure which also uses preferences to weight aspects of welfare: the ICECAP-A measure. I assess the methodology of both approaches in detail and argue that the two goals of a preference measure of welfare can only be satisfied at the expense of making a measure prohibitively costly.     

一种基于预测值量测转换的卡尔曼滤波跟踪算法

在雷达目标跟踪中，系统量测信息通常在球坐标系下获得。为了采用经典卡尔曼滤波算法实现有效目标跟踪，通常采用量测转换方法将非线性量测信息转换到直角坐标系中。针对传统量测转换方法基于量测值计算转换误差统计特性而导致的估计结果有偏问题，提出了一种基于预测值的量测转换方法，并将其与卡尔曼滤波算法相结合，获得了一种基于预测值量测转换的卡尔曼滤波跟踪算法。仿真结果表明，与现有的基于量测转换的卡尔曼滤波算法相比，该算法能在不提高运算量的情况下有效改善目标跟踪效果，跟踪精度提升约20%。     

How Fast are Semiconductor Prices Falling?

The Producer Price Index (PPI) for the U.S. suggests that semiconductor prices have barely been falling in recent years, a dramatic contrast to the rapid declines reported from the mid‐1980s to the early 2000s. This slowdown in the rate of decline is puzzling in light of evidence that the performance of microprocessor units (MPUs) has continued to improve at a rapid pace. Over the course of the 2000s, the MPU prices posted by Intel, the dominant producer of MPUs, became much stickier over the chips' life cycle. As a result of this change, we argue that the matched‐model methodology used in the PPI for MPUs likely started to be biased after the early 2000s and that hedonic indexes can provide a more accurate measure of price change since then. MPU prices fell rapidly through 2004 on every price measure we present, with the PPI declining at an even quicker pace than the hedonic indexes. However, from 2004 to 2009, our preferred hedonic index fell faster than the PPI, and from 2009 to 2013 the gap widened further, with our preferred index falling at an average annual rate of 42 percent, while the PPI declined at only a 6 percent rate. Given that MPUs currently represent about half of U.S. shipments of semiconductors, this difference has important implications for gauging the rate of innovation in the semiconductor sector.     

Lexical Measures of Social Inequality: From Pigou‐Dalton to Hammond

A measure of social inequality is essentially a rational ordering over a space of social distributions. However, different measures, including the most popular ones, may provide very different rankings over the same set of typical distributions. We thus propose an axiomatic approach to inequality measurement mainly based on the Hammond principle, a natural generalization of the Pigou‐Dalton principle, attempting to clarify the true nature of social inequality: the rich get richer and the poor get poorer. Under the standard assumptions of anonymity and scale independence, we show that a social inequality ordering is the leximinimax measure if and only if it satisfies the first Hammond principle, and it is the leximaximin measure if and only if it satisfies the second Hammond principle.     

基于多重交叉残差方法的空间目标旋转特性分析

针对传统的频谱分析和自相关函数反演周期时常出现虚假周期的问题，提出了多重交叉残差法处理空间旋转目标雷达散射截面(Radar Cross Section，RCS)的时间序列。以空间跟踪和监视系统(Space Tracking and Surveillance System,STSS)卫星为例，仿真了空间目标失稳旋转状态，对比了频谱分析、自相关函数和多重交叉残差3种方法的处理结果，发现当目标存在进动运动时多重交叉残差法抗干扰能力更强。在此基础上，进一步研究了不同章动角时多重交叉残差法对旋转周期的反演效果，结果表明章动角大小会影响旋转周期的反演准确度，且章动角达到一定数值后，卫星进动幅度过大导致RCS序列周期性模糊，无法用多重交叉残差法反演出旋转周期。     

微纳卫星组网星间链路设计及关键技术分析

微纳卫星组网应用可大幅度提高航天装备的能力，较单颗大卫星具有明显的优势。首先，介绍了国内外微纳卫星组网的研究情况，以组网侦察应用为例分析了星间测量与通信链路的技术需求，并进行了系统架构和体制设计；其次，在星座设计基础上开展系统仿真，并提出了一体化设备实现方案；最后，分析了微纳卫星组网系统中几项重要的关键技术及其解决方案，对系统的设计和工程实现具有参考价值。     

Productivity Dispersion and Measurement Error

Several reasons have been put forward to explain the high dispersion of productivity across establishments: quality of management, different input usage and market distortions, to name but a few. Although it is acknowledged that a sizable portion of productivity dispersion may also be due to measurement error, little research has been devoted to identifying how much they contribute. We outline a novel procedure for identifying the role of measurement error in explaining the empirical dispersion of productivity across establishments. The starting point of our framework is the errors-in-variable model consisting of a measurement equation and a structural equation for latent productivity. We estimate the variance of the measurement error and subsequently estimate the variance of the latent productivity variable, which is not contaminated by measurement error. Using Norwegian data on the manufacture of food products, we find that about one percent of the measured dispersion stems from measurement error.     

Precision and Manipulation of Non-financial Information: The Curious Case of Environmental Liability

This paper develops a model showing how the environmental liability regime and the precision of the disclosed environmental performance indicator affect managers’ incentives (1) to reduce actual pollution and (2) to manipulate the reported pollution. I assume a company with a separation of ownership and control which can be held liable for environmental damages and distinguish between a negligence regime and strict liability. The results suggest that if there is no manipulation but only a lack of precision of the disclosed environmental performance indicator, a negligence rule induces lower actual pollution levels than strict liability even though a negligence rule is considered to be more lenient. If managers are able to manipulate the disclosed environmental performance indicator, they will do so and actual pollution levels will generally increase. While manipulation makes it easier for shareholders to escape liability under a negligence regime, shareholders suffer from manipulation under strict liability due to higher actual pollution and higher expected damage compensation payments. Therefore, the manipulation level is higher under a negligence regime. My analysis contributes to the environmental performance and disclosure literature by showing that the liability regime is an important determinant affecting environmental reporting and actual pollution decisions.