Zero weights and non-zero slacks:different solution to the same problem

This paper re-assesses three independently developed approaches that are aimed at solving the problem of zero-weights or non-zero slacks in Data Envelopment Analysis (DEA). The methods are weights restricted, non-radial and extended facet DEA models. Weights restricted DEA models are dual to envelopment DEA models with restrictions on the dual variables (DEA weights) aimed at avoiding zero values for those weights; non-radial DEA models are envelopment models which avoid non-zero slacks in the input-output constraints. Finally, extended facet DEA models recognize that only projections on facets of full dimension correspond to well defined rates of substitution/transformation between all inputs/outputs which in turn correspond to non-zero weights in the multiplier version of the DEA model. We demonstrate how these methods are equivalent, not only in their aim but also in the solutions they yield. In addition, we show that the aforementioned methods modify the production frontier by extending existing facets or creating unobserved facets. Further we propose a new approach that uses weight restrictions to extend existing facets. This approach has some advantages in computational terms, because extended facet models normally make use of mixed integer programming models, which are computationally demanding.

Alliance behaviour, absorptive capacity and competitive advantage: a comparative study of biopharmaceutical firms in Europe and the US

In this thesis, we explore the relationship between absorptive capacity and alliances, and their influence on firms’ competitive advantage in the US and European biopharmaceutical sectors. The study undertaken in this thesis is based on data from a large-scale international survey of over 2,500 biopharmaceutical firms in the US, the UK, Germany, France and Ireland. The thesis advanced a conceptual framework, which integrated the multi-dimensions of absorptive capacity, exploration-exploitation alliances, and competitive advantage, into a biopharmaceutical firm’s new product development process. The proposed framework is then tested in the empirical analysis, using truncated models to estimate firms’ sales growth, with zero-inflated negative binominal models capturing the number of alliances in which firms engage, and aspects of realised absorptive capacity analysed by ordinal probit models. The empirical results suggest that both skill-based and exploitation-based absorptive capacity play crucial roles in shaping firms’ competitive advantage, while neither exploratory nor exploitation alliances contribute to the improvement in firms’ competitive position. In terms of the interaction between firms’ absorptive capacity and alliance behaviour, the results suggest that engagement with exploratory alliances depends more strongly on firms’ assimilation capability (skills levels and continuity of R&D activities), while participation in exploitation alliances is more conditional on firms’ relevant knowledge monitoring capability. The results highlight the major differences between the determinants of firms’ alliance behaviour, and competitive advantage in the US and Europe – in the US firms’ skill levels prove more significant in determining firms’ engagement with exploratory alliances, whereas in Europe continuity of R&D proves more important. Correspondingly, while US firms’ engagement with exploitation alliances depends on market monitoring capability, that in Europe is more strongly linked to exploitation-based absorptive capacity. In respect of the determinants of firms’ competitive advantage – in Europe, market monitoring capability, engagement with exploitation alliances, and continuous R&D activities, prove more important, while in the US, it is firms’ market characteristics that matter most.

Macroeconomic determinants of contingent protection:the case of the European Union

Contingent Protection has grown to become an important trade restricting device. In the European Union, protection instruments like antidumping are used extensively. This paper analyses whether macroeconomic pressures may contribute to explain the variations in the intensity of antidumping protectionism in the EU. The empirical analysis uses count data models, applying various specification tests to derive the most appropriate specification. Our results suggest that the filing activity is inversely related to the macroeconomic conditions. Moreover, they confirm existing evidence for the US suggesting that domestic macroeconomic pressures are a more important determinant of contingent protection policy than external pressures.

Statnote 16: fitting a regression line to data

Fitting a linear regression to data provides much more information about the relationship between two variables than a simple correlation test. A goodness of fit test of the line should always be carried out. Hence, ‘r squared’ estimates the strength of the relationship between Y and X, ANOVA whether a statistically significant line is present, and the ‘t’ test whether the slope of the line is significantly different from zero. In addition, it is important to check whether the data fit the assumptions for regression analysis and, if not, whether a transformation of the Y and/or X variables is necessary.

Data methods in optometry. Part 6: fitting a regression line to data

1. Fitting a linear regression to data provides much more information about the relationship between two variables than a simple correlation test. A goodness of fit test of the line should always be carried out. Hence, r squared estimates the strength of the relationship between Y and X, ANOVA whether a statistically significant line is present, and the ‘t’ test whether the slope of the line is significantly different from zero. 2. Always check whether the data collected fit the assumptions for regression analysis and, if not, whether a transformation of the Y and/or X variables is necessary. 3. If the regression line is to be used for prediction, it is important to determine whether the prediction involves an individual y value or a mean. Care should be taken if predictions are made close to the extremities of the data and are subject to considerable error if x falls beyond the range of the data. Multiple predictions require correction of the P values. 3. If several individual regression lines have been calculated from a number of similar sets of data, consider whether they should be combined to form a single regression line. 4. If the data exhibit a degree of curvature, then fitting a higher-order polynomial curve may provide a better fit than a straight line. In this case, a test of whether the data depart significantly from a linear regression should be carried out.

Optical data transmission using periodic in-line all-optical format conversion

We introduce a novel transmission technique of periodic in-line all-optical format conversion between return-to-zero and non-return-to-zero-like aimed at delaying the accumulation of format-specific impairments. A particular realization of this approach using in-line normal dispersion fibre-enhanced nonlinear optical loop mirrors at 40Gbit/s data rate is presented. © 2004 Optical Society of America.

Study of the operating regime for all-optical passive 2R regeneration of dispersion-managed RZ data at 40 Gb/s using in-line NOLMs

In this letter, we numerically demonstrate that the use of inline nonlinear optical loop mirrors in strongly dispersion-managed transmission systems dominated by pulse distortion and amplitude noise can achieve all-optical passive 2R regeneration of a 40-Gb/s return-to-zero data stream. We define the tolerance limits of this result to the parameters of the input pulses.

Optical data transmission using periodic in-line all-optical format conversion

We introduce a novel transmission technique of periodic in-line all-optical format conversion between return-to-zero and non-return-to-zero-like aimed at delaying the accumulation of format-specific impairments. A particular realization of this approach using in-line normal dispersion fibre-enhanced nonlinear optical loop mirrors at 40Gbit/s data rate is presented.

Improving envelopment in Data Envelopment Analysis under variable returns to scale

In a Data Envelopment Analysis model, some of the weights used to compute the efficiency of a unit can have zero or negligible value despite of the importance of the corresponding input or output. This paper offers an approach to preventing inputs and outputs from being ignored in the DEA assessment under the multiple input and output VRS environment, building on an approach introduced in Allen and Thanassoulis (2004) for single input multiple output CRS cases. The proposed method is based on the idea of introducing unobserved DMUs created by adjusting input and output levels of certain observed relatively efficient DMUs, in a manner which reflects a combination of technical information and the decision maker's value judgements. In contrast to many alternative techniques used to constrain weights and/or improve envelopment in DEA, this approach allows one to impose local information on production trade-offs, which are in line with the general VRS technology. The suggested procedure is illustrated using real data. © 2011 Elsevier B.V. All rights reserved.

Information capacity of optical fiber channels with zero average dispersion

We study the statistics of optical data transmission in a noisy nonlinear fiber channel with a weak dispersion management and zero average dispersion. Applying analytical expressions for the output probability density functions both for a nonlinear channel and for a linear channel with additive and multiplicative noise we calculate in a closed form a lower bound estimate on the Shannon capacity for an arbitrary signal-to-noise ratio.

Handling varying amounts of missing data when classifying mental-health risk levels

One of the main challenges of classifying clinical data is determining how to handle missing features. Most research favours imputing of missing values or neglecting records that include missing data, both of which can degrade accuracy when missing values exceed a certain level. In this research we propose a methodology to handle data sets with a large percentage of missing values and with high variability in which particular data are missing. Feature selection is effected by picking variables sequentially in order of maximum correlation with the dependent variable and minimum correlation with variables already selected. Classification models are generated individually for each test case based on its particular feature set and the matching data values available in the training population. The method was applied to real patients' anonymous mental-health data where the task was to predict the suicide risk judgement clinicians would give for each patient's data, with eleven possible outcome classes: zero to ten, representing no risk to maximum risk. The results compare favourably with alternative methods and have the advantage of ensuring explanations of risk are based only on the data given, not imputed data. This is important for clinical decision support systems using human expertise for modelling and explaining predictions.

56 Gb/s multi-band CAP for data center interconnects up to an 80 km SMF

We present the first (to the best of our knowledge) experimental demonstration of a 56 Gb/s multi-band carrierless amplitude and phase modulation (CAP) signal transmission over an 80-km single-mode fiber link with zero overhead pre-FEC signal recovery and enhanced timing jitter tolerance for optical data center interconnects.