或有要求权在公司负债定价中的应用

对公司负债估值的传统的方法是折现现金流的方法,但这种方法也存在很多缺陷.或有要求权的分析方法作为一种全新的方法可以克服折现现金流的方法在某些方面的不足.利用二叉树模型首先分析最简单情形,进而利用动态复制技术分析二叉树为二阶段时的公司负债价值.二阶段二叉树分析完了也就很自然的可以推广到任何阶段的二叉树.从而为公司负债的定价提供一种新的途径.     

Estimating copula densities, using model selection techniques

Recently a new way of modeling dependence has been introduced considering a sequence of parametric copula models, covering more and more dependency aspects and thus giving a closer approximation to the true copula density. The method uses contamination families based on Legendre polynomials. It has been shown that in general after a few steps accurate approximations are obtained. In this paper selection of the adequate number of steps is considered, and estimation of the unknown parameters within the chosen contamination family is established, thus obtaining an estimator of the unknown copula density. There should be a balance between the complexity of the model and the number of parameters to be estimated. High complexity gives a low model error, but a large stochastic or estimation error, while a very simple model gives a small stochastic error, but a large model error. Techniques from model selection are applied, thus letting the data tell us which aspects are important enough to capture into the model. Natural and simple estimators of the involved Fourier coefficients complete the procedure. Theoretical results show that the expected quadratic error is reduced by the selection rule to the same order of magnitude as in a classical parametric problem. The method is applied on a real data set, illustrating that the new method describes the data set very well: the error involved in the classical Gaussian copula density is reduced with no fewer than 50%.     

Teachers’ Activities in Technology-Based Mathematics Lessons

This paper focuses on high school mathematics teachers and what they do when they use digital technology in their lessons. It is essentially a discursive paper but it uses data from a project on teachers using technology to illustrate points. The main aim of the paper is to present an holistic account of factors influencing teachers’ practice. A secondary aim is to present the integration of technology into lessons as a complex issue. Saxe’s four parameter model of goal-linked practice is employed to show how different dimensions of teachers’ activities interrelate in this complex undertaking. The paper ends with a consideration of approaches related to Saxe’s model.     

Modelling Operational Risk Losses with Graphical Models and Copula Functions

The management of Operational Risk has been a difficult task due to the lack of data and the high number of variables. In this project, we treat operational risks as multivariate variables. In order to model them, copula functions are employed, which are a widely used tool in finance and engineering for building flexible joint distributions. The purpose of this research is to propose a new methodology for modelling Operational Risks and estimating the required capital. It combines the use of graphical models and the use of copula functions along with hyper-Markov law. Historical loss data of an Italian bank is used, in order to explore the methodology’s behaviour and its potential benefits.      

A class of transformation rate models for recurrent event data

Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformation rate models for recurrent event data, which uses an additive Aalen model as its covariate dependent baseline. The new models are flexible in that they allow for both additive and multiplicative covariate effects, and some covariate effects are allowed to be nonparametric and time-varying. An estimating procedure is proposed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. Simulation studies and a real data analysis demonstrate that the proposed method performs well and is appropriate for practical use.     

Estimation of Stochastic Frontier Cost Function Using Data Envelopment Analysis: an Application to the AT&T Divestiture

This study develops a new use of data envelopment analysis for estimating a stochastic frontier cost function that is assumed to have two different error components: a one-sided disturbance (representing technical and allocative inefficiencies) and a two-sided disturbance (representing an observational error). The two error components are handled by data envelopment analysis in combination with goal programming/constrained regression. The approach proposed in this study can avoid several statistical assumptions used in conventional methods for estimating a stochastic frontier function. As an important application, this study uses the estimation technique to obtain an AT&T stochastic frontier cost function. As a result, this study measures technical and allocative efficiencies of AT&T production process and review its natural monopoly issue. The estimated stochastic frontier cost function is also compared with the other cost function models used for previous studies concerning the divestiture of the telephone industry.     

A lognormal approximation of activity duration in PERT using two time estimates

The success behind effective project management lies in estimating the time for individual activities. In many cases, these activity times are non-deterministic. In such situations, the conventional method (project evaluation and review technique (PERT)) obtains three time estimates, which are then used to calculate the expected time. In practice, it is often difficult to get three accurate time estimates. A recent paper suggests using just two time estimates and an approximation of the normal distribution to obtain the expected time and variance for that activity. In this paper, we propose an alternate method that uses only two bits of information: the most-likely and either the optimistic or the pessimistic time. We use a lognormal approximation and experimental results to show that our method is not only better than the normal approximation, but also better than the conventional method when the underlying activity distributions are moderately or heavily right skewed.     

Difference equation models for estimating athletic records

This article presents an analysis of the models that are frequently used for estimating the future records for atheletic events. It considers an innovative approach for modeling based on difference equations whose complementary solution yields one of these models. It does this by establishing a relationship between the models and certain difference equations. Moreover, it shows that each of those models can be converted to the exponential model by applying an appropriate substitution. Based on the derived relations it proposes a numerical procedure for estimating the model parameters and discusses its advantages over an existing procedure. This procedure reduces the computational cost and avoids the classical difficulties of non-linear optimization procedures such as the Newton's method. Finally, it tests the proposed numerical procedure using simulated data and applies the procedure to the fastest times for 400 m race.     

Enhancing the inclusion of non-discretionary inputs in DEA

The aim of this paper is to provide an alternative approach for estimating efficiency when a set of decision-making units uses non-discretionary inputs in the productive process. To test the influence of these variables, our proposal uses a multi-stage approach based on Tobit regressions. In order to avoid potential bias, a bootstrap procedure is used to estimate these regressions. This methodology allows enhancing other models previously proposed to introduce non-controllable inputs in data envelopment analysis (DEA) overcoming, thus, some of their main shortcomings. We illustrate our framework with an empirical application on Spanish high schools where non-controllable factors play a major role to explain educational achievements.     

Least median of squares estimation by optimization heuristics with an application to the CAPM and a multi-factor model

For estimating the parameters of models for financial market data, the use of robust techniques is of particular interest. Conditional forecasts, based on the capital asset pricing model, and a factor model are considered. It is proposed to consider least median of squares estimators as one possible alternative to ordinary least squares. Given the complexity of the objective function for the least median of squares estimator, the estimates are obtained by means of optimization heuristics. The performance of two heuristics is compared, namely differential evolution and threshold accepting. It is shown that these methods are well suited to obtain least median of squares estimators for real world problems. Furthermore, it is analyzed to what extent parameter estimates and conditional forecasts differ between the two estimators. The empirical analysis considers daily and monthly data on some stocks from the Dow Jones Industrial Average Index.     

On the identifiability of measurement error in the bifurcating autoregressive model

Huggins and Staudte (1994) considered a mixed linear model for the analysis of cell lineage data and in models for the covariance structure which involved measurement error, it was not immediately clear that the parameters involved were identifiable. Whilst a numerical examination of the Hessian matrix at the estimated parameter values gave some reassurance, this was not theoretically satisfying. Here a matrix formulation of the robust estimating functions of Huggins (1993a, b) as applied in Huggins and Staudte (1994), which include the maximum likelihood estimating functions under the assumption of multivariate normality as a special case, is given along with a direct proof linking identifiability expressed in terms of the estimating functions with the information matrix or its analogue in more general settings. The resulting conditions on the estimating functions may then be checked globally using computer algebra, suggesting a method for establishing identifiability in mixed linear models in general.     

Preface to topics in data envelopment analysis

This paper serves as an introduction to a series of three papers which are directed to different aspects of DEA (Data Envelopment Analysis) as follows: (1) uses and extensions of window analyses' to study DEA efficiency measures with an illustrative applications to maintenance activities for U.S. Air Force fighter wings, (2) a comparison of DEA and regression approaches to identifying and estimating, sources of inefficiency by means of artificially generated data, and (3) an extension of ordinary (linear programming) sensitivity analyses to deal with special features that require attention in DEA. Background is supplied in this introductory paper with accompanying proofs and explanations to facilitate understanding of what DEA provides in the way of underpinning for the papers that follow. An attempt is made to bring readers abreast of recent progress in DEA research and uses. A synoptic history is presented along with brief references to related work, and problems requiring attention are also indicated and possible research approaches also suggested.This research was partly supported by the National Science Foundation and USARI Contract MDA 903-83-K0312, with the Center for Cybernetic Studies, the University of Texas at Austin. It was also partly supported by the IC² Institute at the University of Texas at Austin. Reproduction in whole or in part is permitted for any purpose of the U.S. Government.     

The use of fuzzy variables in pert

In this paper we present a method—called Fpert—for estimating a project completion time in the situation when activity duration times in the project network model are given in the form of fuzzy variables—fuzzy sets on time space. Theoretical foundations of the method as well as results of calculations derived from a simple example are included.     

Analysis of housing prices by GEE and GLMM methodologies: a longitudinal study

The Spanish economic crisis has led to a significant reduction in housing sales, and therefore, there has been a decrease in housing prices. In this paper, we analyze changes in the average housing price throughout Spain. We use quarterly data from a random sample of 150 municipalities from the first quarter (Q1) of 2005, before the financial crisis started, to Q1 2010. Our analysis uses generalized estimating equation and generalized linear mixed model approaches. Data published for Q2, Q3, and Q4 2010 are compared with the data fitted using these models. Finally, the methods are compared with time‐series models. Copyright © 2012 John Wiley & Sons, Ltd.     

Incentive schemes for resolving Parkinson’s Law in project management

Project management is a business process that supports about 30% of the world’s economic activity. Yet projects routinely suffer from the influence of Parkinson’s Law. This behavioural phenomenon routinely results in failure to deliver work that is completed early before its assigned deadline. As a consequence, the late completion of other work is not offset, and overall project performance suffers. Hence, project success rates below 40% are widely reported.Our work uses mechanism design within non-cooperative game theory. A particular issue in the design process is to eliminate the possibility that a project worker with multiple dependent tasks can improve their incentive payment by falsely reporting some of their task completion times. From our review of the academic and business literature of project management, no incentive scheme used in practice accomplishes this.Our results include the design of incentive schemes that eliminate or mitigate Parkinson’s Law. These schemes apply to projects designed under either traditional Critical Path Method (CPM) planning or modern Critical Chain Project Management (CCPM) planning, and are also invulnerable to group strategy. A large-scale computational study validates the resulting benefit to project performance as substantial and also robust across different project characteristics. We also provide what is apparently the first analytical comparison between traditional CPM and modern CCPM planning systems.The incentive schemes we propose are simple and easily implementable. We recognize that performance incentives are structured differently by each organization, but our work provides a flexible basis from which various practical schemes can be designed.     

Tolerance intervals in risk theory

Up to now, the main aim of credibility theory has been to provide statistical models which allow for estimating (net) risk premiums appropriately. In the present note, a simple credibility model based on the percentile principle is introduced. It turns out that there are close connections between the resulting credibility premiums and statistical tolerance limits.     

Estimating the parameters of fatigue curve of a composite material

By using the method of maximum likelihood, the parameters of two versions of a mathematical model for fatigue damage accumulation in a laminate are estimated. The models, which are founded on the Markov chain theory, are very simple: they do not take into account the specific structural features of a composite and therefore cannot provide numerical coincidence with experimental fatigue test data, but they can be used for a nonlinear regression analysis of fatigue curves. A simple method is offered for approximately estimating model parameters, some of which characterize the distribution of the local static strength. By using such models, we can predict the relative changes in fatigue curves from known relative variations in the parameters of static strength and also predict the distribution function of fatigue life in program fatigue tests.Translated from Mekhanika Kompozitnykh Materialov, Vol. 41, No. 1, pp. 109–120, January–February, 2005.     

关于调查问卷中多项选择问题的实用统计分析法探讨

本文讨论了对问卷中多项选择问题的传统处理分析法的不足和局限性．指出以指示变量为工具、简单的积矩相关系数为基础，不但容易得到直观的一目了然的初步结果，还可利用多元统计分析法对数据进行多方面的深层次的分析．     

Provider and customer expectations of successful simulation projects

It is crucial that simulation projects are successful, but defining what is meant by success is far from straightforward, surprising though this may seem. This paper describes an analysis of interviews with simulation providers and customers. What emerges is (a) a definition of success and (b) that the two parties have different views of what constitutes a successful simulation project. The definition of success is based on a four stage model of changing perceptions and it assumes that success is not a simple binary variable, but can vary considerably throughout the life of a project. This suggests that simulation providers need to be careful in managing the expectations of their customers and in how they deliver the work. It is the authors' belief that this may also be true for Operational Research interventions other than those involving computer simulation.