Browsing by Author "Haines, Linda"

Now showing 1 - 9 of 9
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    Detection and down-weighting of outliers in non-normal data: theory and application
    (2014-08-15) Chatora,Tinashe Daniel; Gumedze, Freedom; Little, Francesca; Haines, Linda
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    Estimation of value-at-risk and expected shortfall using copulas
    (2008) Sumbhoolaul, Helina; Haines, Linda
    Includes bibliographical references (leaves 76-77).
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    Geographically weighted regression and an extension
    (2008) Miller, Karen M; Haines, Linda; Thiart, Christien
    Includes abstract. Includes bibliographical references (leaves [72]-75).
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    Investigating 'optimal' kriging variance estimation :analytic and bootstrap estimators
    (2011) Ngwenya, Mzabalazo Z; Thiart, Christien; Haines, Linda
    Kriging is a widely used group of techniques for predicting unobserved responses at specified locations using a set of observations obtained from known locations. Kriging predictors are best linear unbiased predictors (BLUPs) and the precision of predictions obtained from them are assessed by the mean squared prediction error (MSPE), commonly termed the kriging variance.
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    Modelling conditional covariances with orthogonal factor models
    (2011) Jensen, Tracy; Haines, Linda
    The recent sub prime crisis has resulted in an increased focus on risk management and monitoring in the financial industry. One of the essential components of risk management and monitoring is a reliable ex-ante covariance matrix of various financial time series. Therefore a reliable model which can handle a large number of time series is required to calculate an ex-ante or conditional covariance matrix.
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    Multivariate volatility modelling in modern finance
    (2008) Bongers, Martin B; Haines, Linda
    The aim of the study is to ascertain whether the information gained from the more complicated multivariate matrix decomposition models can be used to better forecast the covariance matrix and produce a Value at Risk estimate which more appropriately describes fat-tailed financial time-series.
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    Nonparametric smoothing in extreme value theory
    (2010) Clur, John-Craig; Haines, Linda
    This work investigates the modelling of non-stationary sample extremes using a roughness penalty approach, in which smoothed natural cubic splines are fitted to the location and scale parameters of the generalized extreme value distribution and the distribution of the r largest order statistics. Estimation is performed by implementing a Fisher scoring algorithm to maximize the penalized log-likelihood function. The approach provides a flexible framework for exploring smooth trends in sample extremes, with the benefit of balancing the trade-off between 'smoothness' and adherence to the underlying data by simply changing the smoothing parameter. To evaluate the overall performance of the extreme value theory methodology in smoothing extremes a simulation study was performed.
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    Structural time series modelling for 18 years of Kapenta fishing in Lake Kariba
    (2012) Dalmeyer, Lara; Erni, Birgit; Haines, Linda
    Includes abstract. Includes bibliographical references.
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    Time series analysis of count data with an application to the incidence of cholera
    (2011) Holloway, Jennifer Patricia; Haines, Linda; Leask, Kerry; Elphinstone, Chris
    This dissertation comprises a study into the application of count data time series models to weekly counts of cholera cases that have been recorded in Beira, Mozambique. The study specifically looks at two classes of time series models for count data, namely observation-driven and parameter-driven, and two models from each of these classes are investigated. The autoregressive conditional Poisson (ACP) and double autoregressive conditional Poisson (DACP) are considered under the observation-driven class, while the parameter-driven models used are the Poisson-gamma and stochastic autoregressive mean (SAM) model. An in-depth case study of the cholera counts is presented in which the four selected count data time series models are compared. In addition the time series models are compared to static Poisson and negative binomial regression, thereby indicating the benefits gained in using count data time series models when the counts exhibit serial correlation. In the process of comparing the models, the effect of environmental drivers on the outbreaks of cholera are observed and discussed.