Multiattribute evaluation of greek banking performance

Ordinal regression analysis is proposed as a means for evaluating banking performance over multiple attributes in the presence of non-monotonic preferences. First, a multivariate statistical analysis is applied to measure the banking performance on the basis of financial ratios that derive from the study of financial statements of a sample of Greek banks for the period 1989–1992. Then, an additive utility model is assessed to obtain the final ranking of a representative sample of Greek banks.     

AnO(\sqrt n L) iteration bound primal-dual cone affine scaling algorithm for linear programmingiteration bound primal-dual cone affine scaling algorithm for linear programming

In this paper we introduce a primal-dual affine scaling method. The method uses a search-direction obtained by minimizing the duality gap over a linearly transformed conic section. This direction neither coincides with known primal-dual affine scaling directions (Jansen et al., 1993; Monteiro et al., 1990), nor does it fit in the generic primal-dual method (Kojima et al., 1989). The new method requires main iterations. It is shown that the iterates follow the primal-dual central path in a neighbourhood larger than the conventional neighbourhood. The proximity to the primal-dual central path is measured by trigonometric functions.     

Decomposing capacity utilization in data envelopment analysis: An application to banks in India

This paper briefly reviews the existing methods of capacity utilization in nonparametric framework from economic perspectives, and then suggests an alternative in the light of limitations therein. In the spirit of work by Coelli et al. [Coelli, T.J., Grifell-Tatje, E., Perelman, S., 2002. Capacity utilisation and profitability: A decomposition of short run profit efficiency. International Journal of Production Economics 79, 261–278], we propose two methods, radial and non-radial, to decompose the input-based physical (technological) capacity utilization into various meaningful components viz., technical inefficiency, ray economic capacity utilization and optimal capacity idleness. A case study of Indian banking industry is taken as an example to illustrate the potential application of these two methods of decomposition. Our two broad empirical findings are that first, competition created after financial sector reforms generates high efficiency growth, and reduces excess capacity; second, the cost gap of the short-run cost from the actual cost is higher for the nationalized banks over the private banks indicating that the former banks, though old, do not reflect their learning experience in their cost minimizing behavior.     

Modélisation multi-fluide eulérienne pour la simulation de brouillards denses polydispersés

In this Note, we derive from the kinetic level a Eulerian multi-fluid model for the simulation of polydisperse dense sprays of vaporizing liquid droplets. This yields an extension of the sectional method developed by Tambour et al. and justified by Laurent et al., to dense sprays where droplets of various sizes, and various velocities due to varying inertia, can coalesce. The velocity partial collisional integrals are first evaluated on Gaussian distribution of velocity and covering the whole range of droplet sizes. The final model is obtained by letting the velocity dispersion go to zero, allowing the collisional integrals to be pre-calculated.     

Asymptotic behavior of solutions to Euler–Poisson equations for bipolar hydrodynamic model of semiconductors

In this paper we study the Cauchy problem for 1-D Euler–Poisson system, which represents a physically relevant hydrodynamic model but also a challenging case for a bipolar semiconductor device by considering two different pressure functions and a non-flat doping profile. Different from the previous studies (Gasser et al., 2003 [7], Huang et al., 2011 [12], Huang et al., 2012 [13]) for the case with two identical pressure functions and zero doping profile, we realize that the asymptotic profiles of this more physical model are their corresponding stationary waves (steady-state solutions) rather than the diffusion waves. Furthermore, we prove that, when the flow is fully subsonic, by means of a technical energy method with some new development, the smooth solutions of the system are unique, exist globally and time-algebraically converge to the corresponding stationary solutions. The optimal algebraic convergence rates are obtained.     

Quantum groupoids associated to universal dynamical R-matrices

By using twist construction, we obtain a quantum groupoid D ⊗_q U_qg for any simple Lie algebra g. The underlying Hopf algebroid structure encodes all the information of the corresponding elliptic quantum group—the quasi-Hopf algebras as obtained by Fronsdal, Arnaudon et al. and Jimbo et al.     

行m-NSD随机变量阵列的完全收敛性

本文研究了行m-NSD随机变量阵列的完全收敛性问题.主要利用m-NSD随机变量的Kolmogorov型指数不等式,获得了行m-NSD随机变量阵列的完全收敛性定理,将Hu等（1998）andSung等（2005）的结果从独立情形推广到了m-NSD随机变量阵列.本文的结论同样推广了Chen等（2008）,Hu等（2009）,Qiu等（2011）和Wang等（2014）的结果.     

Joint and supremum distributions in the compound binomial model with Markovian environment

In this paper, we study the compound binomial model in Markovian environment, which is proposed by Cossette, et al. (2003). We obtain the recursive formula of the joint distributions of T, X(T − 1) and |X(T)| (i.e., the time of ruin, the surplus before ruin and the deficit at ruin) by the method of mass function of up-crossing zero points, as given by Liu and Zhao (2007). By using the same method, the recursive formula of supremum distribution is obtained. An example is included to illustrate the results of the model.     

Lower Bounds from State Space Relaxations for Concave Cost Network Flow Problems

In this paper we obtain Lower Bounds (LBs) to concave cost network flow problems. The LBs are derived from state space relaxations of a dynamic programming formulation, which involve the use of non-injective mapping functions guaranteing a reduction on the cardinality of the state space. The general state space relaxation procedure is extended to address problems involving transitions that go across several stages, as is the case of network flow problems. Applications for these LBs include: estimation of the quality of heuristic solutions; local search methods that use information of the LB solution structure to find initial solutions to restart the search (Fontes et al., 2003, Networks, 41, 221–228); and branch-and-bound (BB) methods having as a bounding procedure a modified version of the LB algorithm developed here, (see Fontes et al., 2005a). These LBs are iteratively improved by penalizing, in a Lagrangian fashion, customers not exactly satisfied or by performing state space modifications. Both the penalties and the state space are updated by using the subgradient method. Additional constraints are developed to improve further the LBs by reducing the searchable space. The computational results provided show that very good bounds can be obtained for concave cost network flow problems, particularly for fixed-charge problems.     

Graphs With Least Eigenvalue —2: A New Proof of the 31 Forbidden Subgraphs Theorem

Generalized line graphs were introduced by Hoffman Proc. Calgary Internat. Conf. on Combinatorial Structures and their applications, Gordon and Breach, New York (1970); they were characterized in 1980 by a collection of 31 forbidden induced subgraphs, obtained independently by Cvetkovi et al., Comptes Rendus Math. Rep. Acad. Sci. Canada (1980) and S. B. Rao et al., Proc. Second Symp., Indian Statistical Institute, Calcutta, Lecture Notes in Math., (1981). Here a short new proof of this characterization theorem is given, based on an edge-colouring technique.     

A note on the modified simple equation method applied to Sharma-Tasso-Olver equation

Jawad et al. have applied the modified simple equation method to find the exact solutions of the nonlinear Fitzhugh-Naguma equation and the nonlinear Sharma-Tasso-Olver equation. The analysis of the Sharma-Tasso-Olver equation obtained by Jawad et al. is based on variant of the modified simple equation method. In this paper, we provide its direct application and obtain new 1- soliton solutions.     

(h, Φ)-entropy differential metric

Burbea and Rao (1982a, 1982b) gave some general methods for constructing quadratic differential metrics on probability spaces. Using these methods, they obtained the Fisher information metric as a particular case. In this paper we apply the method based on entropy measures to obtain a Riemannian metric based on (h, )-entropy measures (Salicrú et al., 1993). The geodesic distances based on that information metric have been computed for a number of parametric families of distributions. The use of geodesic distances in testing statistical hypotheses is illustrated by an example within the Pareto family. We obtain the asymptotic distribution of the information matrices associated with the metric when the parameter is replaced by its maximum likelihood estimator. The relation between the information matrices and the Cramér-Rao inequality is also obtained.     

Local existence of energy class solutions for the dirac—klein—gordon equations

Abstract

We show a method to eliminate a type of mixed asymptotics in certain free boundary problems, and give two examples of its application. It appears that these problems cannot be handled by the monotonicity formula of Alt et al. [Alt, H. W., Caffarelli, L. A., Friedman, A. (1984). Variational problems with two phases and their free boundaries. Trans. Am. Math. Soc. 282(2):431–461] or by the more recent monotonicity formula of Caffarelli et al. [Caffarelli, L. A., Jerison, D., Kenig, C. E. (2002). Some new monotonicity theorems with applications to free boundary problems. Ann. Math. (2) 155(2):369–404].     

Incremental analysis for MCDM with an application to group TOPSIS

The study aims to exploit incremental analysis or marginal analysis to overcome the drawbacks of ratio scales utilized in various multi-criteria or multi-attribute decision making (MCDM/MADM) techniques. In the proposed 11-step procedure, multiple criteria of alternatives are first reorganized as two categories – benefits and costs – and decision information will be manipulated separately. The performances of alternatives are then evaluated on their incremental benefit–cost ratio, and the rank can be obtained by applying the group TOPSIS (technique for order preference by similarity to ideal solution) model (Shih et al., 2007). Two representations of cost, i.e., a cost index and utility index, are proposed in the model to better-fit real-world situations. In addition, some considerations on costs and input–output relations are also discussed in order to understand the essentials of incremental analysis. In the final part, a case of robot selection demonstrates the suggested model to be both robust and efficient in a group decision-making environment.     

Mutual funds performance appraisal using stochastic multicriteria acceptability analysis

Mutual fund investors are concerned with the selection of the best fund in terms of performance among the set of alternative funds. This paper proposes an innovative mutual funds performance evaluation measure in the context of multicriteria decision making. We implement a multicriteria methodology using stochastic multicriteria acceptability analysis, on Greek domestic equity funds for the period 2000-2009. Combining a unique dataset of risk-adjusted returns such as Carhart’s alpha with funds’ cost variables, we obtain a multicriteria performance evaluation and ranking of the mutual funds, by means of an additive value function model. The main conclusion is that among employed variables, the sophisticated Carhart’s alpha plays the most important role in determining fund rankings. On the other hand, funds’ rankings are affected only marginally by operational attributes. We believe that our results could have serious implications either in terms of a fund rating system or for constructing optimal combinations of portfolios.     

A Branch-and-Bound Algorithm for Concave Network Flow Problems

In this paper a Branch-and-Bound (BB) algorithm is developed to obtain an optimal solution to the single source uncapacitated minimum cost Network Flow Problem (NFP) with general concave costs. Concave NFPs are NP-Hard, even for the simplest version therefore, there is a scarcity of exact methods to address them in their full generality. The BB algorithm presented here can be used to solve optimally single source uncapacitated minimum cost NFPs with any kind of concave arc costs. The bounding is based on the computation of lower bounds derived from state space relaxations of a dynamic programming formulation. The relaxations, which are the subject of the paper (Fontes et al., 2005b) and also briefly discussed here, involve the use of non-injective mapping functions, which guarantee a reduction on the cardinality of the state space. Branching is performed by either fixing an arc as part of the final solution or by removing it from the final solution. Computational results are reported and compared to available alternative methods for addressing the same type of problems. It could be concluded that our BB algorithm has better performance and the results have also shown evidence that it has a sub-exponential time growth.     

Vitesses de convergence uniforme presque sûre d'estimateurs non-paramétriques de la régression

We obtain rates of strong uniform consistency for some nonparametric regression estimators, including the local linear regression and some wavelet estimators. Our method of proof relies on recent empirical process theory developed by Deheuvels and Mason (Statist. Inference Stoch. Process. 7 (3) (2004) 225–277). To cite this article: D. Blondin et al., C. R. Acad. Sci. Paris, Ser. I 340 (2005).     

Linear codes with few weights from weakly regular plateaued functions

Linear codes with few nonzero weights have wide applications in secret sharing, authentication codes, association schemes and strongly regular graphs. Recently, Wu et al. (2020) obtained some few-weighted linear codes by employing bent functions. In this paper, inspired by Wu et al. and some pioneers' ideas, we use a kind of functions, namely, general weakly regular plateaued functions, to define the defining sets of linear codes. Then, by utilizing some cyclotomic techniques, we construct some linear codes with few weights and obtain their weight distributions. Notably, some of the obtained codes are almost optimal with respect to the Griesmer bound. Finally, we observe that our newly constructed codes are minimal for almost all cases.     

Predictive tools in data mining and k-means clustering: Universal Inequalities

Grouping data into meaningful clusters is very important in data mining. K-means clustering is a fast method for finding clusters in data. The integral inequalities are a predictive tool in data mining and k-means clustering. Many papers have been published on speeding up k-means or nearest neighbor search using inequalities that are specific for Euclidean distance. An extended inequality related to Hölder type for universal integral is obtained in a rather general form. Previous results of Agahi et al. (Results Math, 61:179–194, 2012) are generalized by relaxing some of their requirements, thus closing the series of papers on this topic. Chebyshev’s, Hölder’s, Minkowski’s, Stolarsky’s, Jensen’s and Lyapunov’s type inequalities for the universal integral are obtained.     

A commutative l-monoid for classification with fuzzy attributes

In this paper the properties of an algebraic fuzzy structure are investigated in detail. The structure is suitable for modeling classifications through clusters composed of conventional sets and fuzzy attributes. We show that the structure is an integral commutative l-monoid. The expressive power of the structure is such that several situations can be viewed as classification problems, e.g., fuzzy assessment of students, user modeling for fuzzy hypermedia systems, spaces of the cognitive states of the user of a tutoring system, financial investments, medical diagnoses. The problem of getting the unknown classification beginning from the final classification is deeply investigated and it is shown that the problem is strictly related to the solution of an equation in the monoid. Thus it is possible to construct procedures of the type `what happens if' which permit to attain significant results both on the theoretical side and the applicative one. Finally, by means of this approach, both the absolute and the relative relevance of an attribute are defined and evaluated, given a universe of discourse and a set of classifications. Moreover, this couple of features allow to develop a sophisticated analysis of how a new attribute can be obtained beginning from a set of attributes.