A review of generalized linear models for count data with emphasis on current geospatial procedures
- Authors: Michell, Justin Walter
- Date: 2016
- Subjects: Spatial analysis (Statistics) , Bayesian statistical decision theory , Geospatial data , Malaria -- Botswana -- Statistics , Malaria -- Botswana -- Research -- Statistical methods
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5582 , http://hdl.handle.net/10962/d1019989
- Description: Analytical problems caused by over-fitting, confounding and non-independence in the data is a major challenge for variable selection. As more variables are tested against a certain data set, there is a greater risk that some will explain the data merely by chance, but will fail to explain new data. The main aim of this study is to employ a systematic and practicable variable selection process for the spatial analysis and mapping of historical malaria risk in Botswana using data collected from the MARA (Mapping Malaria Risk in Africa) project and environmental and climatic datasets from various sources. Details of how a spatial database is compiled for a statistical analysis to proceed is provided. The automation of the entire process is also explored. The final bayesian spatial model derived from the non-spatial variable selection procedure using Markov Chain Monte Carlo simulation was fitted to the data. Winter temperature had the greatest effect of malaria prevalence in Botswana. Summer rainfall, maximum temperature of the warmest month, annual range of temperature, altitude and distance to closest water source were also significantly associated with malaria prevalence in the final spatial model after accounting for spatial correlation. Using this spatial model malaria prevalence at unobserved locations was predicted, producing a smooth risk map covering Botswana. The automation of both compiling the spatial database and the variable selection procedure proved challenging and could only be achieved in parts of the process. The non-spatial selection procedure proved practical and was able to identify stable explanatory variables and provide an objective means for selecting one variable over another, however ultimately it was not entirely successful due to the fact that a unique set of spatial variables could not be selected.
- Full Text:
- Date Issued: 2016
- Authors: Michell, Justin Walter
- Date: 2016
- Subjects: Spatial analysis (Statistics) , Bayesian statistical decision theory , Geospatial data , Malaria -- Botswana -- Statistics , Malaria -- Botswana -- Research -- Statistical methods
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5582 , http://hdl.handle.net/10962/d1019989
- Description: Analytical problems caused by over-fitting, confounding and non-independence in the data is a major challenge for variable selection. As more variables are tested against a certain data set, there is a greater risk that some will explain the data merely by chance, but will fail to explain new data. The main aim of this study is to employ a systematic and practicable variable selection process for the spatial analysis and mapping of historical malaria risk in Botswana using data collected from the MARA (Mapping Malaria Risk in Africa) project and environmental and climatic datasets from various sources. Details of how a spatial database is compiled for a statistical analysis to proceed is provided. The automation of the entire process is also explored. The final bayesian spatial model derived from the non-spatial variable selection procedure using Markov Chain Monte Carlo simulation was fitted to the data. Winter temperature had the greatest effect of malaria prevalence in Botswana. Summer rainfall, maximum temperature of the warmest month, annual range of temperature, altitude and distance to closest water source were also significantly associated with malaria prevalence in the final spatial model after accounting for spatial correlation. Using this spatial model malaria prevalence at unobserved locations was predicted, producing a smooth risk map covering Botswana. The automation of both compiling the spatial database and the variable selection procedure proved challenging and could only be achieved in parts of the process. The non-spatial selection procedure proved practical and was able to identify stable explanatory variables and provide an objective means for selecting one variable over another, however ultimately it was not entirely successful due to the fact that a unique set of spatial variables could not be selected.
- Full Text:
- Date Issued: 2016
Bayesian logistic regression models for credit scoring
- Authors: Webster, Gregg
- Date: 2011
- Subjects: Bayesian statistical decision theory Credit scoring systems Regression analysis Logistic regression analysis Monte Carlo method Markov processes Financial institutions
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5574 , http://hdl.handle.net/10962/d1005538
- Description: The Bayesian approach to logistic regression modelling for credit scoring is useful when there are data quantity issues. Data quantity issues might occur when a bank is opening in a new location or there is change in the scoring procedure. Making use of prior information (available from the coefficients estimated on other data sets, or expert knowledge about the coefficients) a Bayesian approach is proposed to improve the credit scoring models. To achieve this, a data set is split into two sets, “old” data and “new” data. Priors are obtained from a model fitted on the “old” data. This model is assumed to be a scoring model used by a financial institution in the current location. The financial institution is then assumed to expand into a new economic location where there is limited data. The priors from the model on the “old” data are then combined in a Bayesian model with the “new” data to obtain a model which represents all the available information. The predictive performance of this Bayesian model is compared to a model which does not make use of any prior information. It is found that the use of relevant prior information improves the predictive performance when the size of the “new” data is small. As the size of the “new” data increases, the importance of including prior information decreases
- Full Text:
- Date Issued: 2011
- Authors: Webster, Gregg
- Date: 2011
- Subjects: Bayesian statistical decision theory Credit scoring systems Regression analysis Logistic regression analysis Monte Carlo method Markov processes Financial institutions
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5574 , http://hdl.handle.net/10962/d1005538
- Description: The Bayesian approach to logistic regression modelling for credit scoring is useful when there are data quantity issues. Data quantity issues might occur when a bank is opening in a new location or there is change in the scoring procedure. Making use of prior information (available from the coefficients estimated on other data sets, or expert knowledge about the coefficients) a Bayesian approach is proposed to improve the credit scoring models. To achieve this, a data set is split into two sets, “old” data and “new” data. Priors are obtained from a model fitted on the “old” data. This model is assumed to be a scoring model used by a financial institution in the current location. The financial institution is then assumed to expand into a new economic location where there is limited data. The priors from the model on the “old” data are then combined in a Bayesian model with the “new” data to obtain a model which represents all the available information. The predictive performance of this Bayesian model is compared to a model which does not make use of any prior information. It is found that the use of relevant prior information improves the predictive performance when the size of the “new” data is small. As the size of the “new” data increases, the importance of including prior information decreases
- Full Text:
- Date Issued: 2011
Cointegration in equity markets: a comparison between South African and major developed and emerging markets
- Authors: Petrov, Pavel
- Date: 2011
- Subjects: Cointegration Stock exchanges -- South Africa Stock exchanges -- Developing countries Stock exchanges -- Developed countries South Africa -- Economic conditions Portfolio management -- South Africa Econometrics Autoregression (Statistics)
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5575 , http://hdl.handle.net/10962/d1005539
- Description: Cointegration has important implications for portfolio diversification. One of these is that in order to spread risk it is advisable to invest in markets that are not cointegrated. Over the last several decades communication technology has made the world a smaller place and hence cointegration in equity markets has become more prevalent. The bulk of research into cointegration focuses on developed and Asian markets, with little research been done on African markets. This study compares the Engle-Granger and Johansen tests for cointegration and uses them to calculate the level of cointegration between South African and other global equity markets. Each market is compared pair-wise with South Africa and the results have been that in general South Africa is cointegrated with other emerging markets but not really with African nor developed markets. Short-run analysis with the error correction was carried out and showed that in general markets respond slowly to any disequilibrium. Innovation accounting methods showed that the country placed first in Cholesky ordering dominates the other one. Multivariate cointegration was carried out using three selections of 4, 6 and 8 market portfolios. One of the markets was SA and the others were all chosen based on the criteria that they are not pair-wise cointegrated with SA. The level of cointegration varied depending on the portfolios, as did the error correction rates, impulse responses and variance decomposition. The one constant was that the USA dominated any portfolio where it was introduced. Recommendations were finally made about which market portfolio an investor should consider as most favourable.
- Full Text:
- Date Issued: 2011
- Authors: Petrov, Pavel
- Date: 2011
- Subjects: Cointegration Stock exchanges -- South Africa Stock exchanges -- Developing countries Stock exchanges -- Developed countries South Africa -- Economic conditions Portfolio management -- South Africa Econometrics Autoregression (Statistics)
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5575 , http://hdl.handle.net/10962/d1005539
- Description: Cointegration has important implications for portfolio diversification. One of these is that in order to spread risk it is advisable to invest in markets that are not cointegrated. Over the last several decades communication technology has made the world a smaller place and hence cointegration in equity markets has become more prevalent. The bulk of research into cointegration focuses on developed and Asian markets, with little research been done on African markets. This study compares the Engle-Granger and Johansen tests for cointegration and uses them to calculate the level of cointegration between South African and other global equity markets. Each market is compared pair-wise with South Africa and the results have been that in general South Africa is cointegrated with other emerging markets but not really with African nor developed markets. Short-run analysis with the error correction was carried out and showed that in general markets respond slowly to any disequilibrium. Innovation accounting methods showed that the country placed first in Cholesky ordering dominates the other one. Multivariate cointegration was carried out using three selections of 4, 6 and 8 market portfolios. One of the markets was SA and the others were all chosen based on the criteria that they are not pair-wise cointegrated with SA. The level of cointegration varied depending on the portfolios, as did the error correction rates, impulse responses and variance decomposition. The one constant was that the USA dominated any portfolio where it was introduced. Recommendations were finally made about which market portfolio an investor should consider as most favourable.
- Full Text:
- Date Issued: 2011
An analysis of neural networks and time series techniques for demand forecasting
- Authors: Winn, David
- Date: 2007
- Subjects: Time-series analysis , Neural networks (Computer science) , Artificial intelligence , Marketing -- Management , Marketing -- Data processing , Marketing -- Statistical methods , Consumer behaviour
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5572 , http://hdl.handle.net/10962/d1004362 , Time-series analysis , Neural networks (Computer science) , Artificial intelligence , Marketing -- Management , Marketing -- Data processing , Marketing -- Statistical methods , Consumer behaviour
- Description: This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method.
- Full Text:
- Date Issued: 2007
- Authors: Winn, David
- Date: 2007
- Subjects: Time-series analysis , Neural networks (Computer science) , Artificial intelligence , Marketing -- Management , Marketing -- Data processing , Marketing -- Statistical methods , Consumer behaviour
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: vital:5572 , http://hdl.handle.net/10962/d1004362 , Time-series analysis , Neural networks (Computer science) , Artificial intelligence , Marketing -- Management , Marketing -- Data processing , Marketing -- Statistical methods , Consumer behaviour
- Description: This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasting targets as well as Time Series forecasts, suggesting that producers could reduce costs by adopting this more effective method.
- Full Text:
- Date Issued: 2007
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