The specification of vector autoregressive moving average models

In this paper we propose a new identification method based on the residual white noise autoregressive criterion (Pukkila et al. , 1990) to select the order of VARMA structures. Results from extensive simulation experiments based on different model structures with varying number of observations and number of component series are used to demonstrate the performance of this new procedure. We also use economic and business data to compare the model structures selected by this order selection method with those identified in other published studies.

On the estimation and comparison of short-rate models using the generalised method of moments

Subsequent to the influential paper of [Chan, K.C., Karolyi, G.A., Longstaff, F.A., Sanders, A.B., 1992. An empirical comparison of alternative models of the short-term interest rate. Journal of Finance 47, 1209-1227], the generalised method of moments (GMM) has been a popular technique for estimation and inference relating to continuous-time models of the short-term interest rate. GMM has been widely employed to estimate model parameters and to assess the goodness-of-fit of competing short-rate specifications. The current paper conducts a series of simulation experiments to document the bias and precision of GMM estimates of short-rate parameters, as well as the size and power of [Hansen, L.P., 1982. Large sample properties of generalised method of moments estimators. Econometrica 50, 1029-1054], J-test of over-identifying restrictions. While the J-test appears to have appropriate size and good power in sample sizes commonly encountered in the short-rate literature, GMM estimates of the speed of mean reversion are shown to be severely biased. Consequently, it is dangerous to draw strong conclusions about the strength of mean reversion using GMM. In contrast, the parameter capturing the levels effect, which is important in differentiating between competing short-rate specifications, is estimated with little bias. (c) 2006 Elsevier B.V. All rights reserved.

A learning approach for performance evaluation of local network

In this paper, a novel approach is developed to evaluate the overall performance of a local area network as well as to monitor some possible intrusion detections. The data is obtained via system utility 'ping' and huge data is analyzed via statistical methods. Finally, an overall performance index is defined and simulation experiments in three months proved the effectiveness of the proposed performance index. A software package is developed based on these ideas.

A parallel implementation of the lattice solid model for the simulation of rock mechanics and earthquake dynamics

The Lattice Solid Model has been used successfully as a virtual laboratory to simulate fracturing of rocks, the dynamics of faults, earthquakes and gouge processes. However, results from those simulations show that in order to make the next step towards more realistic experiments it will be necessary to use models containing a significantly larger number of particles than current models. Thus, those simulations will require a greatly increased amount of computational resources. Whereas the computing power provided by single processors can be expected to increase according to Moore's law, i.e., to double every 18-24 months, parallel computers can provide significantly larger computing power today. In order to make this computing power available for the simulation of the microphysics of earthquakes, a parallel version of the Lattice Solid Model has been implemented. Benchmarks using large models with several millions of particles have shown that the parallel implementation of the Lattice Solid Model can achieve a high parallel-efficiency of about 80% for large numbers of processors on different computer architectures.

A simulation model of cereal-legume intercropping systems for semi-arid regions I. Model development

Cereal-legume intercropping plays an important role in subsistence food production in developing countries, especially in situations of limited water resources. Crop simulation can be used to assess risk for intercrop productivity over time and space. In this study, a simple model for intercropping was developed for cereal and legume growth and yield, under semi-arid conditions. The model is based on radiation interception and use, and incorporates a water stress factor. Total dry matter and yield are functions of photosynthetically active radiation (PAR), the fraction of radiation intercepted and radiation use efficiency (RUE). One of two PAR sub-models was used to estimate PAR from solar radiation; either PAR is 50% of solar radiation or the ratio of PAR to solar radiation (PAR/SR) is a function of the clearness index (K-T). The fraction of radiation intercepted was calculated either based on Beer's Law with crop extinction coefficients (K) from field experiments or from previous reports. RUE was calculated as a function of available soil water to a depth of 900 mm (ASW). Either the soil water balance method or the decay curve approach was used to determine ASW. Thus, two alternatives for each of three factors, i.e., PAR/SR, K and ASW, were considered, giving eight possible models (2 methods x 3 factors). The model calibration and validation were carried out with maize-bean intercropping systems using data collected in a semi-arid region (Bloemfontein, Free State, South Africa) during seven growing seasons (1996/1997-2002/2003). The combination of PAR estimated from the clearness index, a crop extinction coefficient from the field experiment and the decay curve model gave the most reasonable and acceptable result. The intercrop model developed in this study is simple, so this modelling approach can be employed to develop other cereal-legume intercrop models for semi-arid regions. (c) 2004 Elsevier B.V. All rights reserved.

Application of the cross-entropy method to the buffer allocation problem in a simulation-based environment

The buffer allocation problem (BAP) is a well-known difficult problem in the design of production lines. We present a stochastic algorithm for solving the BAP, based on the cross-entropy method, a new paradigm for stochastic optimization. The algorithm involves the following iterative steps: (a) the generation of buffer allocations according to a certain random mechanism, followed by (b) the modification of this mechanism on the basis of cross-entropy minimization. Through various numerical experiments we demonstrate the efficiency of the proposed algorithm and show that the method can quickly generate (near-)optimal buffer allocations for fairly large production lines.

Simulation of the cake formation and growth in cake filtration

A computer model was developed to simulate the cake formation and growth in cake filtration at an individual particle level. The model was shown to be able to generate structural information and quantify the cake thickness, average cake solidosity, filtrate volume, filtrate flowrate for constant pressure filtration or pressure drop across the filter unit for constant rate filtration as a function of filtration time. The effects of particle size distribution and key operational variables such as initial filtration flowrate, maximum pressure drop and initial solidosity were examined based on the simulated results. They are qualitatively comparable to those observed in physical experiments. The need for further development in simulation was also discussed. (c) 2006 Elsevier Ltd. All rights reserved.

A unified friction description and its application to the simulation of frictional instability using the finite element method

This article first summarizes some available experimental results on the frictional behaviour of contact interfaces, and briefly recalls typical frictional experiments and relationships, which are applicable for rock mechanics, and then a unified description is obtained to describe the entire frictional behaviour. It is formulated based on the experimental results and applied with a stick and slip decomposition algorithm to describe the stick-slip instability phenomena, which can describe the effects observed in rock experiments without using the so-called state variable, thus avoiding related numerical difficulties. This has been implemented to our finite element code, which uses the node-to-point contact element strategy proposed by the authors to handle the frictional contact between multiple finite-deformation bodies with stick and finite frictional slip, and applied here to simulate the frictional behaviour of rocks to show its usefulness and efficiency.

Simulation of air traffic controllers' behaviour using the operator choice model

The Operator Choice Model (OCM) was developed to model the behaviour of operators attending to complex tasks involving interdependent concurrent activities, such as in Air Traffic Control (ATC). The purpose of the OCM is to provide a flexible framework for modelling and simulation that can be used for quantitative analyses in human reliability assessment, comparison between human computer interaction (HCI) designs, and analysis of operator workload. The OCM virtual operator is essentially a cycle of four processes: Scan Classify Decide Action Perform Action. Once a cycle is complete, the operator will return to the Scan process. It is also possible to truncate a cycle and return to Scan after each of the processes. These processes are described using Continuous Time Probabilistic Automata (CTPA). The details of the probability and timing models are specific to the domain of application, and need to be specified using domain experts. We are building an application of the OCM for use in ATC. In order to develop a realistic model we are calibrating the probability and timing models that comprise each process using experimental data from a series of experiments conducted with student subjects. These experiments have identified the factors that influence perception and decision making in simplified conflict detection and resolution tasks. This paper presents an application of the OCM approach to a simple ATC conflict detection experiment. The aim is to calibrate the OCM so that its behaviour resembles that of the experimental subjects when it is challenged with the same task. Its behaviour should also interpolate when challenged with scenarios similar to those used to calibrate it. The approach illustrated here uses logistic regression to model the classifications made by the subjects. This model is fitted to the calibration data, and provides an extrapolation to classifications in scenarios outside of the calibration data. A simple strategy is used to calibrate the timing component of the model, and the results for reaction times are compared between the OCM and the student subjects. While this approach to timing does not capture the full complexity of the reaction time distribution seen in the data from the student subjects, the mean and the tail of the distributions are similar.