Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan

The efficiency of decision processes which can be divided into two stages has been measured for the whole process as well as for each stage independently by using the conventional data envelopment analysis (DEA) methodology in order to identify the causes of inefficiency. This paper modifies the conventional DEA model by taking into account the series relationship of the two sub-processes within the whole process. Under this framework, the efficiency of the whole process can be decomposed into the product of the efficiencies of the two sub-processes. In addition to this sound mathematical property, the case of Taiwanese non-life insurance companies shows that some unusual results which have appeared in the independent model do not exist in the relational model. In other words, the relational model developed in this paper is more reliable in measuring the efficiencies and consequently is capable of identifying the causes of inefficiency more accurately. Based on the structure of the model, the idea of efficiency decomposition can be extended to systems composed of multiple stages connected in series.     

Data fusion in multivariate calibration transfer

We report the use of stacked partial least-squares regression and stacked dual-domain regression analysis with four commonly used techniques for calibration transfer to improve predictive performance from transferred multivariate calibration models. The predictive performance from three conventional calibration transfer methods, piecewise direct standardization (PDS), orthogonal signal correction (OSC) and model updating (MUP), requiring standards measured on both instruments, was significantly improved from data fusion either by stacking of wavelet scales or by stacking of spectral intervals, as demonstrated by transfer of calibrations developed on near-infrared spectra of synthetic gasoline. Stacking did not produce as significant an improvement for calibration transfer using a finite impulse response (FIR) filter, but application of SPLS regression to FIR-transferred spectra improves predictive performance of the transferred model.     

Comparative evaluation of software for retention time alignment of gas chromatography/time-of-flight mass spectrometry-based metabonomic data

In chromatography-based metabonomic research, retention time (RT) alignment of chromatographic peaks poses a challenge for the accurate profiling of biomarkers. Although a number of RT alignment software has been reported, the performance of these software packages have not been comprehensively evaluated. This study aimed to evaluate the RT alignment accuracy of publicly available and commercial RT alignment software. Two gas chromatography/mass spectrometry (GC/MS) datasets acquired from a mixture of standard metabolites and human bladder cancer urine samples, were used to assess three publicly available software packages, MetAlign, MZmine and TagFinder, and two commercial applications comprising the Calibration feature and Statistical Compare of ChromaTOF software. The overall RT alignment accuracies in aligning standard compounds mixture were 93, 92, 74, 73 and 42% for Calibration feature, MZmine, MetAlign, Statistical Compare and TagFinder, respectively. Additionally, unique trends were observed for the individual software with regards to the different experimental conditions related to extent and direction of RT shifts. Conflicting performance was observed for human urine samples suggesting that RT misalignments still occurred despite the use of RT alignment software. While RT alignment remains an inevitable step in data preprocessing, metabonomic researchers are recommended to perform manual check on the RT alignment of important biomarkers as part of their validation process.     

Beam reception and data transmission performance analysis of optical taper used in a mobile wireless optical communication

Mobile wireless optical communication system was demonstrated experimentally by using a special solid coupling optical taper. The performance of special solid coupling optical taper and the data transmission performance with 240 Mbit/s were investigated on the rectilinear orbit between the mobile terminal and the fixed terminal. The experimental results shown that the scheme used in the mobile wireless optical communication system is reliable and quite simple, which could be widely used in future.     

基于小波变换和高斯拟合的在线谱图综合处理方法

微小型移动式现场在线检测技术是分析仪器发展的新领域。针对复杂工作环境中谱图存在强噪声干扰、谱峰重叠、不规则峰形等严重影响仪器的定性和定量准确度的瓶颈技术,提出了一种基于小波变换和高斯拟合相结合的谱图在线综合处理方法,用自研的仪器对甲苯和全氟三丁胺两种典型化合物的谱图进行了处理,并与实验室分析仪器普遍应用的算法进行了对比分析。结果表明,综合方法能够有效解决强噪声干扰、谱峰重叠、不规则峰形问题,提高仪器的定性和定量准确性,同时能够实现数据压缩,满足仪器的在线实时检测要求。综合方法处理甲苯特征峰的平均信噪比(SNR)较移动平滑方法提高了1.3倍,峰位误差ΔM降低了3.6倍,处理全氟三丁胺谱图的数据压缩比为197∶1。     

Investigation of data format conversions based on MZI-SOA

Employing a Mach-Zehnder Interferometer (MZI), this paper describes simulation demonstration of an all-optical scheme for data format conversion between non-return-to-zero (NRZ) and return-to-zero (RZ). Data format conversion between NRZ and RZ at 120 Gb/s has been simulated for the first time using an MZI. In addition, we have proposed for the first time data format conversion from NRZ to RZ by using a single SOA in an MZI.     

Scaling characteristics of ocean wave height time series

Fluctuations in the significant wave height can be quantified by using scaling statistics. In this paper, the scaling properties of the significant wave height were explored by using a large data set of hourly series from 25 monitoring stations located off the west coast of the US. Detrended fluctuation analysis (DFA) was used to investigate the scaling properties of the series. DFA is a robust technique that can be used to detect long-range correlations in nonstationary time series. The significant wave height data was analyzed by using scales from hourly to monthly. It was found that a common scaling behavior can be observed for all stations. A breakpoint in the scaling region around 4-5 days was apparent. Spectral analysis confirms this result. This breakpoint divided the scaling region into two distinct parts. The first part was for finer scales (up to 4 days) which exhibited Brown noise characteristics, while the second one showed 1/f noise behavior at coarser scales (5 days to 1 month). The first order and the second order DFA (DFA1 and DFA2) were used to check the effect of seasonality. It was found that there were no differences between DFA1 and DFA2 results, indicating that there is no effect of trends in the wave height time series. The resulting scaling coefficients range from 0.696 to 0.890 indicating that the wave height exhibits long-term persistence. There were no coherent spatial variations in the scaling coefficients.     

Ranking decision-making units using common weights in DEA

Data envelopment analysis (DEA) is a linear programming technique that is used to measure the relative efficiency of decision-making units (DMUs). Liu et al. (2008) [13] used common weights analysis (CWA) methodology to generate a CSW using linear programming. They classified the DMUs as CWA-efficient and CWA-inefficient DMUs and ranked the DMUs using CWA-ranking rules. The aim of this study is to show that the criteria used by Liu et al. are not theoretically strong enough to discriminate among the CWA-efficient DMUs with equal efficiency. Moreover, there is no guarantee that their proposed model can select one optimal solution from the alternative components. The optimal solution is considered to be the only unique optimal solution. This study shows that the proposal by Liu et al. is not generally correct. The claims made by the authors against the theorem proposed by Liu et al. are fully supported using two counter examples.     

Computing economies of vertical integration,economies of scope and economies of scale using partial frontier nonparametric methods

So far, in the nonparametric literature only full frontier nonparametric methods have been applied to search for economies of scope and scale, particularly the data envelopment analysis method (DEA). However, these methods present some drawbacks that might lead to biased results. This paper proposes a methodology based on more robust partial frontier nonparametric methods to look for scope and scale economies. Through this methodology it is possible to assess the robustness of these economies, and in particular to assess the influence that extreme data or outliers might have on them. The influence of the imposition of convexity on the production set of firms was also investigated. This methodology was applied to the water utilities that operated in Portugal between 2002 and 2008. There is evidence of economies of vertical integration and economies of scale in drinking water supply utilities and in water and wastewater utilities operating mainly in the retail segment. Economies of scale were found in water and wastewater utilities operating exclusively in the wholesale, and in some of these utilities diseconomies of scope were also found. The proposed methodology also allowed us to conclude that the existence of some smaller utilities makes the minimum optimal scales go down.     

Maintaining the Regular Ultra Passum Law in data envelopment analysis

The variable returns to scale data envelopment analysis (DEA) model is developed with a maintained hypothesis of convexity in input–output space. This hypothesis is not consistent with standard microeconomic production theory that posits an S-shape for the production frontier, i.e. for production technologies that obey the Regular Ultra Passum Law. Consequently, measures of technical efficiency assuming convexity are biased downward. In this paper, we provide a more general DEA model that allows the S-shape.