1,720,978 research outputs found
Modelling electricity demand in South Africa
No full textEnglish: Peak electricity demand is an energy policy concern for all countries throughout the world, causing blackouts and increasing electricity tariffs for consumers. This calls for load curtailment strategies to either redistribute or reduce electricity demand during peak periods. This thesis attempts to address this problem by providing relevant information through a frequentist and Bayesian modelling framework for daily peak electricity demand using South African data. The thesis is divided into two parts. The first part deals with modelling of short term daily peak electricity demand. This is done through the investigation of important drivers of electricity demand using (i) piecewise linear regression models, (ii) a multivariate adaptive regression splines (MARS) modelling approach, (iii) a regression with seasonal autoregressive integrated moving average (Reg-SARIMA) model (iv) a Reg-SARIMA model with generalized autoregressive conditional heteroskedastic errors (Reg-SARIMA-GARCH). The second part of the thesis explores the use of extreme value theory in modelling winter peaks, extreme daily positive changes in hourly peak electricity demand and same day of the week increases in peak electricity demand. This is done through fitting the generalized Pareto, generalized single Pareto and the generalized extreme value distributions. One of the major contributions of this thesis is quantification of the amount of electricity which should be shifted to off peak hours. This is achieved through accurate assessment of the level and frequency of future extreme load forecasts. This modelling approach provides a policy framework for load curtailment and determination of the number of critical peak days for power utility companies. This has not been done for electricity demand in the context of South Africa to the best of our knowledge. The thesis further extends the autoregressive moving average-exponential generalized autoregressive conditional heteroskedasticity model to an autoregressive moving average exponential generalized autoregressive conditional heteroskedasticity-generalized single Pareto distribution. The benefit of this hybrid model is in risk modelling of under and over demand predictions of peak electricity demand. Some of the key findings of this thesis are (i) peak electricity demand is influenced by the tails of probability distributions as well as by means or averages, (ii) electricity demand in South Africa rises significantly for average temperature values below 180C and rises slightly for average temperature values above 220C and (iii) modelling under and over demand electricity forecasts provides a basis for risk assessment and quantification of such risk associated with forecasting uncertainty including demand variability.Afrikaans: Spitselektrisiteitsaanvraag is vir alle lande regdeur die wˆereld ’n energiebeleidskwessie en veroorsaak verdonkerings en toenemende elektrisiteitstariewe vir allle verbruikers. Dit verg ladinginkortingstrategie¨e om ´of elektrisiteitsaanvraag tydens spitstye te herversprei ´of omdit te verminder. Di´everhandeling poog om hierdie probleem aan te spreek deur toepaslike inligting deur ’n frekwentistiese en Bayes-modelraamwerk vir daaglikse spitselektrisiteitsaanvraag te verskaf met behulp van Suid-Afrikaanse data. Die verhandeling word in twee dele verdeel. Die eerste gedeelte handel oor modellering van korttermyn daaglikse spitselektrisiteitsaanvraag. Dit word bereik deur die belangrike dryfvere van elektrisiteitsaanvraag te ondersoek met behulp van (i) stuksgewyse lineˆere regressiemodelle, (ii) ’nmeerveranderlike aanpassende regressielatmodelbenadering (MARS in Engels), (iii) ’n regressie met seisoenale outoregressiewe ge¨ıntegreerde bewegingsgemiddeld-model (Reg-SARIMA in Engels) en (iv) ’n Reg-SARIMA-modelmet veralgemeende outoregressiewe voorwaardelike heteroskedastiese foute (Reg-SARIMA-GARCH in Engels). Die tweede gedeelte van die verhandeling verken die gebruik van ekstreemwaardeteorie in die modellering van winterspitstye, ekstreem-daaglikse positiewe veranderinge in uurlikse spitselektrisiteitsaanvraag en selfde dag van die week verhogings in spitselektrisiteitsaanvraag. Dit word bereik deur die veralgemeende Pareto, veralgemeende enkel-Pareto en die veralgemeende ekstreemwaardeverdeling, in te pas. Een van die hoofbydraes van die verhandeling is die kwantifisering van die Opsomming aantal elektrisiteit wat na niespitstydperke toe geskuif moet word. Dit word bereik deur akkurate assessering van die vlak en herhaling van toekomsitge ladingvoorspellings. Di´emodelbenadering verskaf ’n beleidsraamwerk vir ladinginkorting en die bepaling van die aantal kritieke spitsdae vir krag-utiliteitsmaatskappye. Dit is, na ons beste wete, nie vir elektrisiteitsaanvraag in die Suid-Afrikaansekonteks gedoen nie. Die verhandeling brei verder die outoregressiewe bewegende gemiddelde eksponensiaal veralgemeende outoregressiewe voorwaardelike heteroskedastisiteitsmodel uit na ’n outoregressiewe bewegende gemiddelde eksponensiaal veralgemeende outoregressiewe voorwaardelike heteroskedastisiteit-veralgemeende enkel-Paretoverdeling. Die voordeel van hierdie hibriede model is in risikomodellering van onder-en ooraanvraagvoorspellings van spitselektrisiteitsaanvraag. Sommige van die hoofbevindinge van die verhandeling is dat (i) spitselektrisiteitsaanvraag be¨ınvloed word deur die waarskynlikheidsverdelings asook deur die gemiddelde, (ii) elektrisiteitsaanvraag in Suid-Afrika vir gemiddelide temperatuurwaardes onder 180C aansienlik vermeerder en vir gemiddelde temperatuurwaardes bo 220C effens vermeerder en (iii) modellering van onder-en ooraavraag-elektrisiteitsvoorspellings ’n basis vir risiko-assessering en kwantifisering van sulke risikos wat verband hou met voorspellingsonsekerheid, insluitend aanvraagveranderlikheid, verskaf
Modelling and analysing risk in precious metals
No full textThesis (Ph.D.(Statistics))--University of the Free State, 2018The prices of precious metals are volatile and financial market participants are interested in knowing the downside of holding precious metals in their portfolios. Risk management tools such as Value-at-Risk (VaR) are highly dependent on the underlying distributional assumption. Identifying a distribution that may best capture all the aspects of the given financial data can provide immense advantages to both investors and risk managers. In the analysis and modelling of financial returns, there are stylised facts that are observed. These include volatility clustering, heavy-tails, asymmetry, conditional heavy tails and long range dependence (long memory). In this study, we investigated the stylised facts of gold, platinum and silver returns. We thus propose models that are able to capture their empirical features. The models capture extreme tails of profit and loss distributions and improve the estimation of Value-at-Risk (VaR) of precious metal prices returns. Firstly, we evaluate the performance of existing heavy-tailed and flexible distributions in modelling extreme risk for precious metal returns. The heavy-tailed and flexible distributions used are: Generalised Hyperbolic Distributions (GHDs), Generalised Lambda Distribution (GLD), Stable Distribution (SD), Generalised Pareto Distribution (GPD), Generalised Extreme Value Distribution (GEVD), Pearson type-IV Distribution (PIVD), Symmetrical Student-t Distribution (STD) and Skewed Studentt Distribution (SSTD). Secondly, we couple ARMA-GARCH models and ARMAAPARCH models with heavy-tailed and flexible distributions. We fit the models to precious metal returns and evaluate their relative performance in estimating Valueat-Risk (VaR) using a number of conditional assumptions. The proposed models performed favourably when compared with the APARCH models with a Student-t distribution and the APARCH models with a skewed Student-t distribution usually used in the literature. This provides financial analysts with an alternative distributional scheme to be used in economic modelling. Thirdly, because all daily precious metal price returns exhibit volatility clustering, heavy tails, asymmetry and long range dependence, we fit the long-memory GARCH models under the GHDs, the GPD, the GEVD, the SD, the STD, the SSTD, the GLD and the PIVD assumptions to our price return data. The Anderson-Darling test is used to check for model adequacy. Kupiec likelihood ratio tests and Christoffersen conditional coverage tests are also used in this study to evaluate objectively whether VaR model is adequate. The backtesting results confirm that the long-memory GARCH-heavy-tailed models are adequate for improving risk management assessments and hedging strategies in the highly volatile metal markets. ARFIMA-HYGARCH, ARFIMA-FIGARCH and ARFIMA-FIAPARCH models with PIVD, Normal-Inverse Gaussian Distribution (NIGD), full GHD, FMKL GLD and Generalised Hyperbolic Student-t Distribution (GHStD) innovations are found to be suitable for VaR estimation of precious metals, thereby providing a good alternative candidate for modelling financial returns
Continuous-time Markov modelling of the effects of treatment regimens on HIV/AIDS immunology and virology
No full textThesis (Ph.D.(Mathematical Statistics))--University of the Free State, 2019As the Human immunodeficiency virus (HIV) enters the human body, its main target is the CD4+ cell, which it turns into a factory that produces millions of other HIV particles, thus compromising the immune system and resulting in opportunistic infections, for example tuberculosis (TB). Combination Anti-retroviral therapy (cART) has become the standard of care for patients with HIV infection and has led to the reduction in acquired immunodeficiency syndrome (AIDS) related morbidity and mortality, an increase in CD4+ cell counts and a decrease in viral load count to undetectable levels. In modelling HIV/AIDS progression in patients, researchers mostly deal with either viral load only or CD4+cellcountsonly, as they expect these two variables to be collinear. The purpose of this study is to fit a continuous-time Markov model that best describes mortality of HIV infected patients on cART by eventually including both CD4+ cell counts monitoring and viral load monitoring in a single model after treating for collinearity of these variables using the Principal Component approach. Acohortof320HIVinfectedpatientsoncARTfollowedupat a Wellness Clinic in Bela Bela, South Africa, is used in this thesis. These patients are administered with a triple therapy of two nucleoside reverse transcriptase inhibitor (NRTIs) and one non-nucleoside reverse transcriptase inhibitor (NNRTI). The thesis is divided into five sections. In the first section, a continuous-time homogeneous Markov model based on CD4+ cell count states is fitted. The model is used to analyse the effects of tuberculosis (TB) co-infection on the immunologic progression of HIV/AIDS patients on cART. TB co-infection was of interest because it is an opportunistic infection that takes advantage of the compromised immune system. Results from this section showed that once TB is diagnosed prior to treatment initiation and managed, mortality rates are reduced. However, if TB is diagnosed during the course of treatment, it increases the rates of immune deterioration in patients, leading to high rates of mortality. Therefore, this section proposes the need for routine TB screening before treatment initiation and a tevery stage of the follow up period, to avoid loss of lives. The goal of cART is not only to boost the immune system but also to suppress the viral load to undetectable levels. Thus, in the second section, a non-homogeneous continuous-time Markov model based on viral load states is fitted. This model helped in revealing possibilities of viral rebound among patient son cART. Although there were no significant gender differences on HIV/AIDS virology, the model explained the progression of patients better than the model based on CD4+ cell count fitted in the first section. In the third section, determinants of viral rebound are analysed. Viral rebound was notable mainly after patients had attained a viral load suppressed to the levels between 50 copies/mL and 10 000 copies/mL. The major attributes of viral rebound were non-adherence, lactic acid, resistance to treatment, and different combination therapy such as AZT-3TC-LPV/r and FTC-TDF-EFV. This section suggests the need to closely monitor HIV patients to ensure attainment of undetectable viral load (below 50 copies/mL) during the first six months of treatment uptake, as this reduces chances of viral rebound, leading to life gain by HIV/AIDS patients. The fourth section compares the use of viral load count and CD4+cell count in monitoring HIV/AIDS disease progression on patients receiving cART in order to establish the superiority of viral load over CD4+ cell count. This was done by fitting two separate models, one for CD4+ cell count states and the other one for viral load states. Comparison of the fitted models were based on percentage prevalence plots for the fitted model and for the observed data and likelihood ratio tests. The test confirmed that viral load monitoring is superior compared to CD4+cell count monitoring. Viral load monitoring is very good at detecting virologic failure, thereby avoiding unnecessary switches of treatment lines. However, this section suggests the use of both CD4+cellcount monitoring and viral load monitoring because CD4+ cell count monitoring helps in managing possibilities of the development of opportunistic infections. In the fifth section, continuous-time homogeneous Markov models are fitted, including both CD4+ cell count monitoring and viral load monitoring in one model. Since these variables are assumed to be collinear, principal component analysis was used to treat for the collinearity among these two variables. The models are fitted in such a way that when Markov states are based on CD4+ cell count, the principal component of viral load is included as a covariate, and when the Markov states are based on viral load, the principal component of CD4+cell count is included as a covariate. Results from the models show an improvement in the power of the continuous-time Markov model to explain and predict mortality when both CD4+cellcount and viral load routine monitoring are included in one model
Modelling mean annual rainfall for Zimbabwe
No full textRainfall has a substantial influence on agriculture, food security, infrastructure development,
water quality and the economy. Zimbabwe, like most other Southern
African countries, has distinctive meteorological features which are characterized
by a high variability of temporal and spatial rainfall distributions, flash floods and
prolonged drought periods. Because people struggle to adapt to these diverse rainfall
patterns, a better understanding of rainfall characteristics, its distribution and
potential predictors will help mitigate the effects of these adverse weather conditions.
The aim of this thesis is to develop an early warning tool that can help predict
a drought and/or flash flood in Zimbabwe, and to estimate the amount of rainfall
during the year. In this thesis, mean annual rainfall figures from 1901 to 2015 obtained
from 40 rainfall stations scattered throughout Zimbabwe were used.
The thesis consists of three sections. In the first section, appropriate statistical models
are applied to a set of annual rainfall figures that have been divided by 12 to
produce a mean annual rainfall figure for the year with a view towards finding potential
predictors for rainfall in Zimbabwe. Monthly-based indicator variables associated
with the Southern Oscillation Index (SOI) and the standardised Darwin sea
level pressure readings (SDSLP) were considered as predictor variables with the SOI
and SDSLP readings for August (two months before the onset of the rainfall season)
producing the most important predictor variables for future rainfall in Zimbabwe.
In the second part of the thesis, several characteristics associated with the mean
annual rainfall for Zimbabwe are studied using an appropriately fitted theoretical
probability distribution. More specifically, the annual rainfall figures from 1901 to
2009 were used to fit a gamma, lognormal and log-logistic distribution to the annual
rainfall data. The relative performance of the fitted distributions were then
assessed using the following goodness-of-fit tests, namely; the relative root mean
square error (RRMSE), relative mean absolute error (RMAE) and the probability plot
correlation coefficient (PPCC). All the fitted distributions, however, were not able to
adequately predict periods of extreme rainfall. Extreme value distributions such as
generalised extreme value and generalised Pareto distributions were then fitted to
the mean annual rainfall data. The possibility that periods of extreme rainfall may
be time-dependent and be influenced by weather/climate change drivers was then
considered. This study shows that, although rainfall extremes for Zimbabwe are not
time-dependent, they are highly influenced SDSLP anomalies for April.
In the third and last part of this thesis, we categorized rainfall data using a drought
threshold value of 570 mm. We compared the relative performance of the logistic
regression model in estimating drought probabilities for Zimbabwe with that of
a generalised extreme value regression model for binary data. The department of
meteorological services in Zimbabwe uses 75% of normal annual rainfall (usually
a 30-year time series data) to declare a drought year. Results show that the GEVD
regression model with SDSLP anomaly for April is the best performing model and
can be used to predict drought probabilities for Zimbabwe
Optimal portfolio selection with uncertain implicit transaction costs in a dynamic stochastic framework
No full textThis thesis proposes scenario-based approaches and decision models for some problem contexts in investment decision-making which include (i) optimal portfolio investment in periods of economic booms and recessions, (ii) the incorporation of uncertainty in implicit transaction costs incurred in initial trading and in subsequent rebalancing of portfolios, and (iii) the development of a strategy that captures uncertainty in stock prices and in corresponding implicit trading costs by way of scenarios. The method ological advances of the thesis offer several novel insights into the above decision problems. Firstly, the mean absolute deviation model is developed and extended into a stochastic multi-stage model that incorporates uncertainty of implicit transaction costs, asset returns and risk in optimal portfolio selection. This methodology allows investors and investment managers to choose optimal portfolios realising the impact of associated uncertain implicit transaction costs. Secondly, a stochastic multi-stage trading cost model is developed that also takes into account uncertainty of implicit transaction costs, assets’ returns and portfolio risk. This strategy generates optimal portfolios by minimising total implicit transaction costs incurred. Thirdly, a multi-stage stochastic optimal portfolio policy that minimises maximum downside risk in the presence of uncertain implicit transaction costs is proposed. This strategy is appropriate for a risk-averse and conservative investor who is highly concerned about the performance of his portfolio in an economic recession environment. Fourthly, a dynamic stochastic methodology in optimal portfolio selection that maximises upside deviations (investment opportunities) and minimises maximum downside risk while taking into account uncertain implicit transaction costs incurred in initial trading and recourse times is developed. Lastly, the mean-variance model is extended to become multi-period and to incorporate uncertainty in implicit transaction costs, asset returns and portfolio risk. All the proposed models capture uncertainty in implicit transaction costs, portfolio return and risk by way of scenarios
A survey on participation and attitude to sports among undergraduate students in junior residences at the University of the Free State
No full textThe main objective of this study is to assess and quantify participation in sporting activities by students and to determine the factors influencing students’ intentions to participate or not to participate in sports at the University of the Free State. The data are obtained from interviewing students participating or not participating in various sporting codes available at the University of the Free State (main campus in Bloemfontein, South Africa). A systematic random sampling technique was used as the interviewing team knocked on every fifth door in a given residence to ensure that all corners of each residence were reached. The students found at the residence at that particular time, were asked to fill in the questionnaire. Tables and charts are used for illustration of results. T-tests, F-tests, Principal component analysis, Cluster comparison analysis and Item analysis are also performed for further analysis. Three hundred and eight students (308) (61% females and 39% males) living in junior residences were interviewed for this research. The majority of participants (75%) were non-whites (blacks, coloured, and Asians); this was in line with the University of the Free State enrolment structure of the year 2011 (75% non-whites and 25% whites). The reasons provided by the participants for their participation in sporting activities were indicated as keeping fit (91%), releasing of stress (89.35%), gaining a feeling of wellbeing (83%), increasing in physical abilities (81%) and previous school sports involvement (67%). Students from second academic year upwards mostly raised the positive response that they relied on regular exercise to achieve academic success. The researcher concludes that certain variables, namely gender, age group, race, marital status preferred language of study, faculty of study, academic year of study, previous school sport participation, current sport participation, participated sporting codes, reasons for sport participation and reasons for non-sport participation for students, are the most important variables that the Kovsie Sport and management of sports, should focus on in order to encourage students to participate in sporting activities.
Through sports, students are also able to interact with one another and participate in different sporting codes offered by the university
The capability approach and measurement: operationalizing capability indicators in higher education
No full textThe thesis contributes to work in the field of operational measurement of Human Capabilities. Although a number of studies have examined the challenges posed in the measurement of Human Capabilities, there has not been a focus on the empirical merits of the methods and methodologies followed in identification and measurement of valuable capabilities especially in the Higher Education context. To this end, this study provides insights into the identification of valuable student capabilities through an exposition of the methods which can be followed to create and measure robust indicators of student capabilities. A quantitative inquiry determines which Human capabilities students in Higher Education institutions have reason to value and the results of this process are compared to a theoretical student capabilities literature. The thesis advocates for a human development approach over a human capital approach in evaluating the wellbeing of students. The study is significant in that it aids policy and decision makers in Higher Education to identify what students value and thus be in a position to fashion curricula, programmes and policies in a way which best benefits the subjects. To achieve the above mentioned goal, the thesis draws substantially on the work of Paul Anand, Amartya Sen, Flavio Comim, Enrica Chiappero Martinetti, Ingrid Robeyns, Melanie Walker and Sabina Alkire, among others, who have researched and advanced in the field of operational measurement of human capabilities in the Higher Education environment
Bayesian analysis of process capability indices for single and multiple sources of variability
No full textEnglish: Process capability index (process performance index) -relates the specification limits to
the performance of a process, it reduces complex information about the performance of a
process to a single number. A capability index is a dimensionless measure of relative
variability. In this thesis, Bayesian statistics is employed to simulate and estimate most of
the widely used process capability indices.
In Bayesian analysis, we assume that we have prior knowledge or information or opinion
about parameters of a statistical distribution and very often in practice we do. We then
attach a distribution to this belief. Parameters do not really have a distribution, parameters
are constants, and so a prior distribution is a way of expressing our belief or opinion on
our parameters. A posterior distribution is the belief distribution of the parameters after
the outcomes of experiments (data) have been observed. There is now an updated belief
distribution in light of the information from the data.
Bayesian inference is shown to have a number of advantages. A full Bayesian analysis
provides a natural way of taking into account all sources of uncertainty in the estimation
of the parameters. Uncertainty about the true value of the process capability index is
incorporated into the analysis through the choice of a prior distribution. The most familiar
element of the Bayesian school is the use of the non-informative (objective) prior
distribution, designed to be minimally informative in some sense. The most famous of
these is the Jeffrey’s-rule prior and is utilised throughout the thesis. Scientists hold up
objectivity as the ideal of science. Reference priors are a refinement of the Jeffrey’s-rule
priors for higher dimensional problems that have proven to be remarkably successful. The
probability matching prior is recommended because it is designed to produce posterior
credible intervals which are asymptotically identical to their frequentist counterparts.
The Bayesian simulation procedure employs the posterior distribution of the parameters
in doing the simulations. The procedure is also shown to be useful and comparable to
existing classical statistical procedures in solving the supplier selection problem.
Data arising from multiple sources of variability are very common in practice. Virtually
all industrial processes exhibit between-batch and within-batch components of variation.
In some cases the between-batch (or between subgroup) component is viewed as part of
the common-cause-system for the process. A process capability index in more general
settings is developed using Cpl as a point of reference. Cpl is a single variance index and
is adapted to give 2 and 3 variance components indices. The variance component model
proves to be suitable for handling multiple sources of variability capability indices.
Again, Bayesian simulation methods prove to be useful in handling these multiple
sources of variability indices.
Results show that the Bayesian simulation approach is just as good if not better than the
standard classical statistics approach in assessing the capability of an industrial process.
The added advantage of the Bayesian approach is that, from the posterior distribution of
the capability indices, we are in a position to obtain quantiles, credible regions and
perform other inferential tasks.Afrikaans: Prosesgeskiktheidsanalise verwys na die moontlikheid om die Bayes-simulasiebenadering
toe te pas op prosesgeskiktheidsindekse soos onder andere Cp , Cpk , Pp en Ppk . In hierdie
verhandeling word Bayes-statistiek gebruik om die meeste van die
prosesgeskiktheidsindekse te simuleer en te beraam.
In Bayes-analise neem ons aan dat ons prior kennis of inligting of ‘n opinie het
aangaande parameters van ‘n statistiese verdeling, soos die geval dikwels in die praktyk
is. ‘n Verdeling kan dan aan hierdie oortuiging gekoppel word. Parameters is konstantes
en het nie regtig ‘n verdeling nie, dus is ‘n priorverdeling ‘n manier om ons opinie of
oortuiging aangaande parameters uit te druk. ‘n Posteriorverdeling is ‘n
oortuigingsverdeling van die parameters nadat die uitkomste of eksperimente (data)
waargeneem is. Daar is nou ‘n opgedateerde oortuigingsverdeling in die lig van die
inligting uit die data bekom.
Bayes-inferensie het ‘n hele aantal voordele. ‘n Volledige Bayes-analise voorsien ‘n
natuurlike manier om alle bronne van onsekerheid met die beraming van die parameters
in ag te neem. Onsekerheid oor die werklike waarde van die prosesgeskiktheidsindeks
word in die analise ingesluit deur middel van die keuse van ‘n priorverdeling. Die mees
bekende element van die Bayesskool is die gebruik van die objektiewe priorverdeling,
wat ontwerp is om minimale inligting in ‘n sekere sin te gee. Die mees gewildste een is
die Jeffreys-reël prior wat deurgaans in die verhandeling gebruik word. Wetenskaplikes
hou objektiwiteit as die ideaal van wetenskap voor. Verwysingspriors is ‘n verfyning van
die Jeffreys-reël priors vir hoër dimensionele probleme wat reeds as suksesvol beskou
word. Die waarskynlikheidsgepaste prior word aanbeveel omdat dit ontwerp is om
posterior kredietwaardigheidsintervalle te lewer wat assimptoties identies is aan hulle
frekwentistiese teenpartye.
Die Bayes-simulasieprosedure gebruik die posteriorverdeling om die simulasies uit te
voer. Die prosedure het getoon dat dit geskik en vergelykbaar is met bestaande klassieke
statistiese procedures om die verskaffer-seleksieprobleem op te los.
Data wat uit meervoudige bronne van variasie voortspruit is baie algemeen in die
praktyk. Letterlik alle industriële prosesse toon tussengroep en binnegroep komponente
van variasie. In sommige gevalle word die tussengroepkomponent beskou as deel van die
algemeen-oorsaak-sisteem van die proses. ‘n Prosesgeskiktheidsindeks in meer algemene
omstandighede is ontwikkel deur Cpl as ‘n puntverwysing te gebruik. Cpl is ‘n enkel
variansie-indeks en is aangepas om 2 en 3 variansiekomponentindekse te gee. Daar is
bewys dat die variansiekomponentmodel geskik is vir die hantering van meervoudige
bronne van variasiegeskiktheidsindekse. Weereens kan bewys word dat Bayessimulasiemetodes
geskik is vir die hantering van hierdie meervoudige bronne van
variasie-indekse.
Resultate toon dat die Bayes-simulasiebenadering net so goed, indien nie beter nie, is as
die standaard klassieke statistiekbenadering om die vermoë van die industriële proses te
assesseer. ‘n Bykomende voordeel van die Bayesbenadering is dat, vanuit die
priorverdeling van die geskiktheidsindekse, die moontlikheid geskep word om kwantiele
en kredietwaardigheidsintervalle te bekom, asook om ander inferensiële take uit te voer
Regression-SARIMA modelling of daily peak electricity demand in South Africa
No full textIn this paper, seasonal autoregressive integrated moving average (SARIMA) and regression with SARIMA errors (regression-SARIMA) models are developed to predict daily peak electricity demand in South Africa using data for the period 1996 to 2009. The performance of the developed models is evaluated by comparing them with Winter’s triple exponential smoothing model. Empirical results from the study show that the SARIMA model produces more accurate short-term forecasts. The regression-SARIMA modelling framework captures important drivers of electricity demand. These results are important to decision makers, load forecasters and systems operators in load flow analysis and scheduling of electricity
A stochastic multi-stage trading cost model in optimal portfolio selection
No full textWe propose a multi-stage stochastic trading cost model in optimal portfolio selection. This strategy captures uncertainty in implicit transaction costs incurred by an investor during initial trading and in subsequent rebalancing of the portfolio. We assume that implicit costs are stochastic as are asset returns. We use mean absolute deviation as our risk and apply the model to securities on the Johannesburg Stock Market. The model generates optimal portfolios by minimizing total implicit transaction costs incurred. It provides least-cost optimal portfolios whose net wealths are better than those gener- ated by the mean-variance, minimax and mean absolute deviation models
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