A novel technique of object ranking and classification under ignorance: An application to the corporate failure risk problem

This paper exposits a novel technique for the ranking and classification of objects to a particular state. Each object is described by measurements from a number of variables which may offer different levels of support for the individual objects to be associated with the two states, a given hypothesis and not the hypothesis. The Dempster–Shafer theory of evidence is a central component of this technique. This allows for a measure of concomitant ignorance, which may encompass the precision of the individual measurements as well as the possible ambiguity of their influence in the subsequent classification of objects. The level of ignorance present influences the utilisation of the technique as a tool for the ranking or classification of objects. A simplex plot method of representing data allows a clear visual representation (interpretation) to the degree of interaction of the support from the variables to the ranking or classification of the objects. To illustrate the proposed technique, the application considered here is the elucidation of the risk of corporate failure of a number of companies. Subsequently, each variable (financial and non-financial) may offer support for the ranking and classification of companies to between the extreme states of being a failed or non-failed company. A comparison on the ranking and classification of companies is made with a traditional multivariate discriminant analysis.     

Ecological site classification: An application of clustering

This short paper takes up the problem of El-Shishiny and Ghabbour¹ in order to highlight some of the problems in data analysis oriented at recognition of basic data structures, of hierarchy or partition type, with vagueness in initial data and/or the procedure applied. A response to the considered ‘soil fauna taxa versus Sudan sites’ problem is also suggested.     

Incremental accelerated gradient methods for SVM classification: study of the constrained approach

We investigate constrained first order techniques for training support vector machines (SVM) for online classification tasks. The methods exploit the structure of the SVM training problem and combine ideas of incremental gradient technique, gradient acceleration and successive simple calculations of Lagrange multipliers. Both primal and dual formulations are studied and compared. Experiments show that the constrained incremental algorithms working in the dual space achieve the best trade-off between prediction accuracy and training time. We perform comparisons with an unconstrained large scale learning algorithm (Pegasos stochastic gradient) to emphasize that our choice can remain competitive for large scale learning due to the very special structure of the training problem.     

Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates

There are different ways to allow the voters to express their preferences on a set of candidates. In ranked voting systems, each voter selects a subset of the candidates and ranks them in order of preference. A well-known class of these voting systems are scoring rules, where fixed scores are assigned to the different ranks and the candidates with the highest score are the winners. One of the most important issues in this context is the choice of the scoring vector, since the winning candidate can vary according to the scores used. To avoid this problem, Cook and Kress [W.D. Cook, M. Kress, A data envelopment model for aggregating preference rankings, Management Science 36 (11) (1990) 1302–1310], using a DEA/AR model, proposed to assess each candidate with the most favorable scoring vector for him/her. However, the use of this procedure often causes several candidates to be efficient, i.e., they achieve the maximum score. For this reason, several methods to discriminate among efficient candidates have been proposed. The aim of this paper is to analyze and show some drawbacks of these methods.     

An application of time series methods to financial guarantee insurance

This article comprises a summary of a study made in Finland concerning solvency issues in financial guarantee insurance. The time fluctuation of bankruptcy intensity is analyzed by fitting Box-Jenkins type models to empirical data, and this fluctuation is combined with the variation in the number of claims and the individual claim sizes, based on empirical claim size distribution. The estimated models are used to evaluate, for example, the variance of the claims ratio and of the solvency ratio of the financial guarantee insurer. The variation range of the solvency ratio and the appropriate premium level are discussed with numerical examples.     

Multidimensional empirical quantiles: An application to the study of the weather in Brazil

This paper is the result of the practical need to specify local weather conditions that could help to design locally adapted telecommunication systems in Brazil. Several practical alternatives for defining multidimensional empirical quantiles are considered, as well as their possible applications, and the statistical problems involved.     

An MCDM approach to production analysis: An application to irrigated farms in Southern Spain

The analysis of the decision-making process in agricultural enterprises is approached with the development of a methodology based upon two stages: firstly we enlarge our knowledge of the system under study by performing grouping operation - cluster analysis - of the farm enterprises. The result of this stage is to classify farms according to crop pattern, which is the basis for the second stage of analysis: the system is studied by solving a weighted goal programming to approach the weights given by different farmers to the objectives of the decision process. This methodology is applied to two nearby but different irrigation units in Southern Spain, and we found that there was an important degree of heterogeneity in production plans explained by differences in objective weights.     

因子分析在居民消费结构的变动分析中的应用

以往在研究居民消费结构方面,使用较多的方法是恩格尔系数和线性支出系统,但在进行消费结构变动分析时,由于指标多,且指标体系中各指标之间存在着此消彼涨、此低彼高的现象。笔者认为因子分析模型正是解决这一问题的有力工具,并利用它对浙江城镇居民消费结构变动进行了实证分析。     

Performance evaluation of fuzzy classification methods designed for real time application

This paper proposes a comparative appraisal of the fuzzy classification methods which are Fuzzy C-Means, K Nearest Neighbours, method based on Fuzzy Rules and Fuzzy Pattern Matching method. It presents the results we obtained in applying those methods on three types of data that we present in the second part of this article. The classification rate and the computing times are compared from a method to another. This paper describes the advantages of the fuzzy classifiers for an application to a diagnosis problem. To finish it proposes a synthesis of our study which can constitute a base to choose an algorithm in order to apply it to a process diagnosis in real time. It shows how we can associate unsupervised and supervised methods in a diagnosis algorithm.     

Competitive analysis of manufacturing plants: An application to the US processed food industry

We present a model-based approach for competitive analysis of manufacturing plants. As part of this approach, we propose the application of Operational Competitiveness Ratings Analysis (OCRA) to measure the competitiveness of plants in terms of their relative inefficiency. We then present a conceptual framework to classify and identify the drivers of plant competitiveness in terms of decisions related to plant structure and infrastructure. Finally, we demonstrate the application of this model-based approach to conduct competitive analysis of plants in the US processed food industry.     

The application of parameter sensitivity analysis methods to inverse simulation models

Knowledge of the sensitivity of inverse solutions to variation of parameters of a model can be very useful in making engineering design decisions. This article describes how parameter sensitivity analysis can be carried out for inverse simulations generated through approximate transfer function inversion methods and also through the use of feedback principles. Emphasis is placed on the use of sensitivity models and the article includes examples and a case study involving a model of an underwater vehicle. It is shown that the use of sensitivity models can provide physical understanding of inverse simulation solutions that is not directly available using parameter sensitivity analysis methods that involve parameter perturbations and response differencing.     

An application of optimization theory to the study of equilibria for games: a survey

This contribution is a survey about potential games and their applications. In a potential game the information that is sufficient to determine Nash equilibria can be summarized in a single function on the strategy space: the potential function. We show that the potential function enable the application of optimization theory to the study of equilibria. Potential games and their generalizations are presented. Two special classes of games, namely team games and separable games, turn out to be potential games. Several properties satisfied by potential games are discussed and examples from concrete situations as congestion games, global emission games and facility location games are illustrated.     

Sensitivity analysis with finite changes: An application to modified EOQ models

In this work, we introduce a new method for the sensitivity analysis of model output in the presence of finite changes in one or more of the exogenous variables. We define sensitivity measures that do not rest on differentiability. We relate the sensitivity measures to classical differential and comparative statics indicators. We prove a result that allows us to obtain the sensitivity measures at the same cost of one-variable-at-a-time methods, thus making their estimation feasible also for computationally intensive models. We discuss in detail the derivation of managerial insights formulating a procedure based on the concept of “Settings”. The method is applied to the sensitivity analysis of a discrete change in optimal order quantity following a jump in the exogenous variables of a non-linear programming inventory model.     

Sensitivity analysis of mixed integer programs: An application to environmental policy making

We consider a class of mixed integer programs in which the concave objective function and the constraint matrix are held fixed while some of the right hand side (RHS) coefficients are varied. An efficient iterative algorithm is developed for performing the above sensitivity analysis. A practical application of this class of programs is encountered in environmental policy making and accordingly it is used in illustrating the operation of the algorithm.     

Sensitivity analysis of efficiency and Malmquist productivity indices: An application to Spanish savings banks

Hypothesis testing and statistical precision in the context of non-parametric efficiency and productivity measurement have been investigated since the early 1990s. Recent contributions focus on this matter through the use of resampling methods—i.e., bootstrapping techniques. However, empirical evidence is still practically non-existent. This gap is more noticeable in the case of banking efficiency studies, where the literature is immense. In this work, we explore productivity growth and productive efficiency for Spanish savings banks over the (initial) post-deregulation period 1992–1998 using Data Envelopment Analysis (DEA) and bootstrapping techniques. Results show that productivity growth has occurred, mainly due to improvement in production possibilities, and that mean efficiency has remained fairly constant over time. The bootstrap analysis yields further evidence, as for many firms productivity growth, or decline, is not statistically significant. With regard to efficiency measurement, the bootstrap reveals that the disparities in the original efficiency scores of some firms are lessened to a great extent.