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Full-Text Articles in Physical Sciences and Mathematics
Evaluating The Volatility Forecasting Performance Of Best Fitting Garch Models In Emerging Asian Stock Markets, Chaiwat Kosapattarapim, Yan-Xia Lin, Michael Mccrae
Evaluating The Volatility Forecasting Performance Of Best Fitting Garch Models In Emerging Asian Stock Markets, Chaiwat Kosapattarapim, Yan-Xia Lin, Michael Mccrae
Faculty of Informatics - Papers (Archive)
While modeling the volatility of returns is essential for many areas of finance, it is well known that financial return series exhibit many non-normal characteristics that cannot be captured by the standard GARCH model with a normal error distribution. But which GARCH model and which error distribution to use is still open to question, especially where the model that best fits the in-sample data may not give the most effective out-of-sample volatility forecasting ability. Approach: In this study, six simulated studies in GARCH(p,q) with six different error distributions are carried out. In each case, we determine the best fitting GARCH …
A Fundamental Analysis Of Continuous Flow Bioreactor And Membrane Reactor Models With Non-Competitive Product Inhibition. Iii. Linear Inhibition, Mark I. Nelson, Wei X. Lim
A Fundamental Analysis Of Continuous Flow Bioreactor And Membrane Reactor Models With Non-Competitive Product Inhibition. Iii. Linear Inhibition, Mark I. Nelson, Wei X. Lim
Faculty of Informatics - Papers (Archive)
The steady-state production of a product produced through the growth of microorganisms in a continuous flow bioreactor is presented. A generalised reactor model is used in which both the classic well-stirred bioreactor and the idealised membrane bioreactor are considered as special cases. The reaction is assumed to be governed by Monod growth kinetics subject to non-competitive product inhibition. Inhibition is modelled as a decreasing linear function of the product concentration with a finite cut-off. This reaction scheme is well documented in the literature, although a stability analysis of the governing equations has not previously been presented. The steady-state solutions for …
A Fundamental Analysis Of Continuous Flow Bioreactor And Membrane Reactor Models With Tessier Kinetics, M I. Nelson, E Balakrishnan, H S. Sidhu
A Fundamental Analysis Of Continuous Flow Bioreactor And Membrane Reactor Models With Tessier Kinetics, M I. Nelson, E Balakrishnan, H S. Sidhu
Faculty of Informatics - Papers (Archive)
In this research we analyze the steady-state operation of a continuous flow bioreactor, with or without recycle, and an idealized or nonidealized continuous flow membrane reactor. The model extends to include a fixed bed reactor where a fraction of the biomass is detached by the flow. The reaction is assumed to be governed by Tessier growth kinetics. We show that a flow reactor with idealized recycle has the same performance as an idealized membrane reactor and that the performance of a nonidealized membrane reactor is identical to that of an appropriately defined continuous flow bioreactor with nonidealized recycle. The performance …
A Spatial Analysis Of Multivariate Output From Regional Climate Models, Stephan Sain, Reinhard Furrer, Noel A. Cressie
A Spatial Analysis Of Multivariate Output From Regional Climate Models, Stephan Sain, Reinhard Furrer, Noel A. Cressie
Faculty of Informatics - Papers (Archive)
Climate models have become an important tool in the study of climate and climate change, and ensemble experiments consisting of multiple climate-model runs are used in studying and quantifying the uncertainty in climate-model output. However, there are often only a limited number of model runs available for a particular experiment, and one of the statistical challenges is to characterize the distribution of the model output. To that end, we have developed a multivariate hierarchical approach, at the heart of which is a new representation of a multivariate Markov random field. This approach allows for flexible modeling of the multivariate spatial …
Initial Values In Estimation Procedures For State Space Models (Ssms), Raed Alzghool, Yan-Xia Lin
Initial Values In Estimation Procedures For State Space Models (Ssms), Raed Alzghool, Yan-Xia Lin
Faculty of Informatics - Papers (Archive)
In this paper, we will focus on State Space Models(SSMs), especially the stochastic volatility model, and lookfor a standard approach for assigning initial values in theQuasi-Likelihood (QL) and Asymptotic Quasi-Likelhood (AQL)estimation procedures.
Pricing Of Volatility Derivatives Using 3/2-Stochastic Models, Joanna Goard
Pricing Of Volatility Derivatives Using 3/2-Stochastic Models, Joanna Goard
Faculty of Informatics - Papers (Archive)
Analytic solutions are found for prices of both variance and volatility swaps and VIX options under new 3/2- stochastic models for the dynamics of the underlying assets. The main features of the new stochastic differential equations are an empirically validated cv3/2 diffusion term, a nonlinear drift providing a balancing effect of a stronger mean reversion with high volatility, and for the case of the variance and volatility swaps, a free function of time as a moving target in the reversion term, allowing additional flexibility for model calibration against market data.
Vibration Control Of Seat Considering Vehicle Suspension And Human-Body Models, Haiping Du, Weihua Li, Nong Zhang
Vibration Control Of Seat Considering Vehicle Suspension And Human-Body Models, Haiping Du, Weihua Li, Nong Zhang
Faculty of Informatics - Papers (Archive)
Vehicle seat suspension is one of very important components to provide ride comfort, in particular, commercial vehicles, to reduce driver fatigue due to long hours driving. This paper presents a study on active control of seat suspension to reduce vertical vibration transmitted from uneven road profile to driver body. The control problem will be firstly studied by proposing an integrated seat suspension model which includes vehicle chassis suspension, seat suspension, and driver body model. This is a new concept in the field of study because most of the current active and semi-active seat suspension studies only consider seat suspension or …
A Mathematical Model For The Biological Treatment Of Industrial Wastewater In A Reactor Cascade, Rubayyi Turki Alqahtani, Mark I. Nelson, Annette L. Worthy
A Mathematical Model For The Biological Treatment Of Industrial Wastewater In A Reactor Cascade, Rubayyi Turki Alqahtani, Mark I. Nelson, Annette L. Worthy
Faculty of Informatics - Papers (Archive)
Many industrial processes, particularly in the food industry, produce slurries or wastewaters containing high concentrations of biodegradable organic materials. Before these contaminated wastewaters can be discharged the concentration of the biodegradable organic pollutant must be reduced. One way to do this is to pass the wastewater through a bioreactor containing biomass which grows through consumption of the pollutant. Anaerobic conditions are often favoured for the processing of waste materials with high levels of biodegradable organic pollutants as these can be removed with low investment and operational costs. We investigate the steady state effluent concentration leaving a cascade of two reactors. …
Some Mathematical Models Arising In Nano- And Bio-Technology, Yue Chan
Some Mathematical Models Arising In Nano- And Bio-Technology, Yue Chan
Faculty of Informatics - Papers (Archive)
In this thesis, three mechanical models arising from nanoscale and biological systems are investigated, namely the dynamics of various nanostructures, the axial buckling of carbon nanotubes and nanopeapods, and the worm-like chain model for stretched semi-flexible molecules and the utilization of such a model for investigating molecular stretching in the connective tissue extracellular matrix.
On The Analysis Of Background Subtraction Techniques Using Gaussian Mixture Models, Abdesselam Bouzerdoum, Azeddine Beghdadi, Son Lam Phung, Philippe L. Bouttefroy
On The Analysis Of Background Subtraction Techniques Using Gaussian Mixture Models, Abdesselam Bouzerdoum, Azeddine Beghdadi, Son Lam Phung, Philippe L. Bouttefroy
Faculty of Informatics - Papers (Archive)
In this paper, we conduct an investigation into background subtraction techniques using Gaussian Mixture Models (GMM) in the presence of large illumination changes and background variations. We show that the techniques used to date suffer from the trade-off imposed by the use of a common learning rate to update both the mean and variance of the component densities, which leads to a degeneracy of the variance and creates “saturated pixels”. To address this problem, we propose a simple yet effective technique that differentiates between the two learning rates, and imposes a constraint on the variance so as to avoid the …
Formative Versus Reflective Measurement Models: Two Applications Of Formative Measurement, T. Coltman, T. M. Devinney, D. F. Midgley, S. Venaik
Formative Versus Reflective Measurement Models: Two Applications Of Formative Measurement, T. Coltman, T. M. Devinney, D. F. Midgley, S. Venaik
Faculty of Informatics - Papers (Archive)
This paper presents a framework that helps researchers to design and validate both formative and reflective measurement models. The framework draws from the existing literature and includes both theoretical and empirical considerations. Two important examples, one from international business and one from marketing, illustrate the use of the framework. Both examples concern constructs that are fundamental to theory-building in these disciplines, and constructs that most scholars measure reflectively. In contrast, applying the framework suggests that a formative measurement model may be more appropriate. These results reinforce the need for all researchers to justify, both theoretically and empirically, their choice of …
Can Superior Crm Capabilities Improve Performance In Banking, T. R. Coltman
Can Superior Crm Capabilities Improve Performance In Banking, T. R. Coltman
Faculty of Informatics - Papers (Archive)
The market enthusiasm generated around investment in customer relationship management (CRM) technology is in stark contrast to the nay-saying by many academic and business commentators. This raises an important research question concerning the extent to which banks should continue to invest in CRM technology. Drawing on field interviews and a survey of senior bank executives the results reveal that a superior CRM capability can deliver improved performance. The paper then demonstrates that in order to be most successful, CRM programs require a combination of technical, human and business capabilities.
Flexible Spatial Models For Kriging And Cokriging Using Moving Averages And The Fast Fourier Transform (Fft), Jay M. Ver Hoef, Noel A. Cressie, Ronald P. Barry
Flexible Spatial Models For Kriging And Cokriging Using Moving Averages And The Fast Fourier Transform (Fft), Jay M. Ver Hoef, Noel A. Cressie, Ronald P. Barry
Faculty of Informatics - Papers (Archive)
Models for spatial autocorrelation and cross-correlation depend on the distance and direction separating two locations, and are constrained so that for all possible sets of locations, the covariance matrices implied from the models remain nonnegative-definite. Based on spatial correlation, optimal linear predictors can be constructed that yield complete maps of spatial fields from incomplete and noisy spatial data. This methodology is called kriging if the data are of only one variable type, and it is called cokriging if it is of two or more variable types. Historically, to satisfy the nonnegative-definite condition, cokriging has used coregionalization models for cross-variograms, even …
Size And Power Considerations For Testing Loglinear Models Using Divergence Test Statistics, Noel A. Cressie, L Pardo, M Del Carmen Pardo
Size And Power Considerations For Testing Loglinear Models Using Divergence Test Statistics, Noel A. Cressie, L Pardo, M Del Carmen Pardo
Faculty of Informatics - Papers (Archive)
In this article, we assume that categorical data are distributed according to a multinomial distribution whose probabilities follow a loglinear model. The inference problem we consider is that of hypothesis testing in a loglinear-model setting. The null hypothesis is a composite hypothesis nested within the alternative. Test statistics are chosen from the general class of divergence statistics. This article collects together the operating characteristics of the hypothesis test based on both asymptotic (using large-sample theory) and finite-sample (using a designed simulation study) results. Members of the class of power divergence statistics are compared, and it is found that the Cressie-Read …
Spatial Mixture Models Based On Exponential Family Conditional Distributions, M Kaiser, Noel A. Cressie, J Lee
Spatial Mixture Models Based On Exponential Family Conditional Distributions, M Kaiser, Noel A. Cressie, J Lee
Faculty of Informatics - Papers (Archive)
Spatial statistical models are applied in many problems for which dependence in observed random variables is not easily explained by a direct scientific mechanism. In such situations there may be a latent spatial process that acts to produce the observed spatial pattern. Scientific interest often centers on the latent process and the degree of spatial dependence that characterizes it. Such latent processes may be thought of as spatial mixing distributions. We present methods for the specification of flexible joint distributions to model spatial processes through multi-parameter exponential family conditional distributions. One approach to the analysis of these models is Monte …
Asymptotic Properties Of Maximum (Composite) Likelihood Estimators For Partially Ordered Markov Models, Hsin-Cheng Huang, Noel A. Cressie
Asymptotic Properties Of Maximum (Composite) Likelihood Estimators For Partially Ordered Markov Models, Hsin-Cheng Huang, Noel A. Cressie
Faculty of Informatics - Papers (Archive)
Partially ordered Markov models (POMMs) are Markov random fields (MRFs) with neighborhood structures derivable from an associated partially ordered set. The most attractive feature of POMMs is that their joint distributions can be written in closed and product form. Therefore, simulation and maximum likelihood estimation for the models is quite straightforward, which is not the case in general for MRF models. In practice, one often has to modify the likelihood to account for edge components; the resulting composite likelihood for POMMs is similarly straightforward to maximize. In this article, we use a martingale approach to derive the asymptotic properties of …
Minimum Phi Divergence Estimator And Hierarchical Testing In Loglinear Models, Noel A. Cressie, Leandro Pardo
Minimum Phi Divergence Estimator And Hierarchical Testing In Loglinear Models, Noel A. Cressie, Leandro Pardo
Faculty of Informatics - Papers (Archive)
In this paper we consider inference based on very general divergence measures, under assumptions of multinomial sampling and loglinear models. We define the minimum phi divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. This estimator is then used in a phi divergence goodness-of-fit statistic, which is the basis of two new statistics for solving the problem of testing a nested sequence of loglinear models.
Mine Boundary Detection Using Partially Ordered Markov Models, Xia Hua, Jennifer Davidson, Noel A. Cressie
Mine Boundary Detection Using Partially Ordered Markov Models, Xia Hua, Jennifer Davidson, Noel A. Cressie
Faculty of Informatics - Papers (Archive)
Detection of objects in images in an automated fashion is necessary for many applications, including automated target recognition. In this paper, we present results of an automated boundary detection procedure using a new subclass of Markov random fields (MRFs), called partially ordered Markov models (POMMs). POMMs offer computational advantages over general MRFs. We show how a POMM can model the boundaries in an image. Our algorithm for boundary detection uses a Bayesian approach to build a posterior boundary model that locates edges of objects having a closed loop boundary. We apply our method to images of mines with very good …
Models And Inference For Clustering Of Locations Of Mines And Minelike Objects, Noel A. Cressie, Andrew B. Lawson
Models And Inference For Clustering Of Locations Of Mines And Minelike Objects, Noel A. Cressie, Andrew B. Lawson
Faculty of Informatics - Papers (Archive)
Mines and mine-like objects are distributed throughout an area of interest. Remote sensing of the area form an aircraft yields image data that represent the superposition of electromagnetic emissions from the mines and mine-like objects. In this article we build a hierarchical statistical model for the reconstruction of mien locations given a point pattern of the superposition of mines and mine-like objects. It is shown how inference on the mine locations can be obtained using Markov chain Monte Carlo methods.
Texture Analysis Using Partially Ordered Markov Models, Jennifer Davidson, Ashit Talukder, Noel A. Cressie
Texture Analysis Using Partially Ordered Markov Models, Jennifer Davidson, Ashit Talukder, Noel A. Cressie
Faculty of Informatics - Papers (Archive)
Texture is a phenomenon in image data that continues to receive wide-spread interest due to its broad range of applications. The paper focuses on but one of several ways to model textures, namely, the class of stochastic texture models. the authors introduce a new spatial stochastic model called partially ordered Markov models, or POMMs. They show how POMMs are a generalization of a class of models called Markov mesh models, or MMMs, that allow an explicit closed form of the joint probability, just as do MMMs. While POMMs are a type of Markov random field model (MRF), the general MRFs …