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Abstract: This paper reveals the latency errors in the manufacturing and assembly of the toroidal drive and integrates these errors in the coordinate systems and the mathematical models of the meshing equations for the conjugate surfaces between the planet worm-gear and the sun-worm and between the planet worm-gear and the stationary internal toroidal gear. The paper further develops the tooth profile model and the contact curves with the latency errors and examines the error effect on the helix model and the helix lead angle model. In addition, two kinds of limit curve model and the induced normal curvature models are further derived. This provides a comprehensive study of error effect on the meshing characteristics of the novel drive. [Copyright &y& Elsevier]

Many production systems consist of a sequence of processes or stages. For these systems, relational network DEA can be used and an overall system efficiency (equal to the efficiency of the different processes) can be computed. However, there can be alternative solutions that give different estimations of the process efficiencies and therefore lead to different decompositions of the overall system efficiency. It is not obvious which efficiency decomposition to use. In this paper, it is shown how a Nash bargaining game can be used to compute point estimates of the efficiency of the processes for multistage systems. The proposed approach extends and improves over existing approaches for production systems with just two stages. The rationality principles behind the proposed solution approach are presented and an interesting interpretation of the resulting efficiency decomposition is provided. The fact that this rigorous solution approach leads to such a simple and elegant efficiency decomposition should facilitate its adoption by Expert and Intelligent Systems practitioners. [ABSTRACT FROM AUTHOR]

Abstract: We give an overview of random matrix theory (RMT) with the objective of highlighting the results and concepts that have a growing impact in the formulation and inference of statistical models and methodologies. This paper focuses on a number of application areas especially within the field of high-dimensional statistics and describes how the development of the theory and practice in high-dimensional statistical inference has been influenced by the corresponding developments in the field of RMT. [Copyright &y& Elsevier]

Abstract: This paper proposes a model of prior ignorance about a scalar variable based on a set of distributions . In particular, a set of minimal properties that a set of distributions should satisfy to be a model of prior ignorance without producing vacuous inferences is defined. In the case the likelihood model corresponds to a one-parameter exponential family of distributions, it is shown that the above minimal properties are equivalent to a special choice of the domains for the parameters of the conjugate exponential prior. This makes it possible to define the largest (that is, the least-committal) set of conjugate priors that satisfies the above properties. The obtained set is a model of prior ignorance with respect to the functions (queries) that are commonly used for statistical inferences; it is easy to elicit and, because of conjugacy, tractable; it encompasses frequentist and the so-called objective Bayesian inferences with improper priors. An application of the model to a problem of inference with count data is presented. [Copyright &y& Elsevier]

Abstract: Causal knowledge based on causal analysis can advance the quality of decision-making and thereby facilitate a process of transforming strategic objectives into effective actions. Several creditable studies have emphasized the usefulness of causal analysis techniques. Partial least squares (PLS) path modeling is one of several popular causal analysis techniques. However, one difficulty often faced when we commence research is that the causal direction is unknown due to the lack of background knowledge. To solve this difficulty, this paper proposes a method that links the Bayesian network and PLS path modeling for causal analysis. An empirical study is presented to illustrate the application of the proposed method. Based on the findings of this study, conclusions and implications for management are discussed. [Copyright &y& Elsevier]

Abstract: This paper investigates the asymptotic properties of the likelihood ratio statistics for testing homogeneity in normal mixture models with unknown means and variances. The asymptotic null distribution of the ordinary likelihood ratio statistic is under the conditions that the means are bounded, the variances are in a compact space away from 0 and , and the mixture coefficients are away from 0. The asymptotic null distribution of a modified likelihood ratio statistic is also under the conditions that the means are bounded, and the variances are in a compact space away from 0 and . [Copyright &y& Elsevier]

Abstract: Many mathematical models of physical phenomena that have been proposed in recent years require more general spaces than manifolds. When taking into account the symmetry group of the model, we get a reduced model on the (singular) orbit space of the symmetry group action. We investigate quantization of singular spaces obtained as leaf closure spaces of regular Riemannian foliations on compact manifolds. These contain the orbit spaces of compact group actions and orbifolds. Our method uses foliation theory as a desingularization technique for such singular spaces. A quantization procedure on the orbit space of the symmetry group–that commutes with reduction–can be obtained from constructions which combine different geometries associated with foliations and new techniques originated in Equivariant Quantization. The present paper contains the first of two steps needed to achieve these just detailed goals. [Copyright &y& Elsevier]

Abstract: The Bayesian analysis based on the partial likelihood for Cox''s proportional hazards model is frequently used because of its simplicity. The Bayesian partial likelihood approach is often justified by showing that it approximates the full Bayesian posterior of the regression coefficients with a diffuse prior on the baseline hazard function. This, however, may not be appropriate when ties exist among uncensored observations. In that case, the full Bayesian and Bayesian partial likelihood posteriors can be much different. In this paper, we propose a new Bayesian partial likelihood approach for many tied observations and justify its use. [Copyright &y& Elsevier]

Abstract: Nguyen [1996. An algorithmic approach to constructing supersaturated designs. Technometrics 38, 69–73] and Tang and Wu [1997. -optimality of supersaturated designs. Statist. Sinica 7, 929–939] independently derived a lower bound for the value of an N run, m factor supersaturated design (SSD). This bound can be achieved only if m is a multiple of when or if m is an even multiple of when . One important question is whether Nguyen–Tang–Wu bound can be achieved in all of these cases. In this paper, based on a construction method by , we present a theoretical method for finding as many positive integers t as possible such that there is an -optimal SSD achieving the Nguyen–Tang–Wu bound with N runs and factors when and factors when . This method is applied to the cases. [Copyright &y& Elsevier]

Abstract: The first motivation of this work is to take into account model uncertainty in sensitivity analysis (SA). We present with some examples, a methodology to treat uncertainty due to a mutation of the studied model. Development of this methodology has highlighted an important problem, frequently encountered in SA: how to interpret sensitivity indices when random inputs are non-independent? This paper suggests a strategy for the problem of SA of models with non-independent random inputs. We propose a new application of the multidimensional generalization of classical sensitivity indices, resulting from group sensitivities (sensitivity of the output of the model to a group of inputs), and describe an estimation method based on Monte-Carlo simulations. Practical and theoretical applications illustrate the interest of this method. [Copyright &y& Elsevier]

In this paper, we consider a class of optimal control problems in which the dynamical system involves both switching and impulsive controls. The number of switching times are supposed to be finite and, without loss of generality, to occur together with the impulses. We show that this class of optimal control problems is equivalent to a special class of optimal parameter selection problems. The gradient formulae for the cost functional and the constraint functional are derived. On this basis, the control parameterization is shown to be applicable for numerical solution. A numerical example is solved for illustration. [Copyright &y& Elsevier]

Abstract: In this discussion paper, I present my views on the role on mathematical statistics for solving computer vision problems. [Copyright &y& Elsevier]