Dynamic Factor Model and Artificial Neural Network Models: To Combine Forecasts or Combine Models?

In this chapter, we evaluate the forecasting performance of the model combination and forecast combination of the dynamic factor model (DFM) and the artificial neural networks (ANNs). For the model combination, the factors that are extracted from a large dataset are used as additional input to the ANN model that produces the factor-augmented artificial neural network (FAANN). Linear and nonlinear forecasts combining methods are used to combine the DFM and the ANN forecasts. The results of the best combining method are compared to the forecasts result of the FAANN model. The models are applied to forecast three time series variables using large South African monthly data. The out-of-sample root-mean-square error (RMSE) results show that the FAANN model yields substantial improvement over the individual and best combined forecasts from the DFM and ANN forecasting models and the autoregressive AR benchmark model. Further, the Diebold-Mariano test results also confirm the superiority of the FAANN model forecast’s performance over the AR benchmark model and the combined forecasts

Indirect child mortality estimation technique to identify trends of under-five mortality in Ethiopia

Background: In sub-Saharan African countries, the chance of a child dying before the age of five years is high. The problem is similar in Ethiopia, but it shows a decrease over years.Methods: The 2000; 2005 and 2011 Ethiopian Demographic and Health Survey results were used for this work. The purpose of the study is to detect the pattern of under-five child mortality overtime. Indirect child mortality estimation technique is adapted to examine the under-five child mortality trend in Ethiopia.Results: From the result, it was possible to see the trend of under-five child mortality in Ethiopia. The under-five child mortality shows a decline in Ethiopia.Conclusion: From the study, it can be seen that there is a positive correlation between mother and child survival which is almost certain in any population. Therefore, this study shows the trend of under-five mortality in Ethiopia and decline over time.Keywords: EDHS, under-five mortality, parity, indirect technique, CEB, children survivin

Tuberculosis risk factors in South Africa, 2008 to 2017: A Generalised Estimating Equations approach

Background: Although, death due to tuberculosis has been on the decline. In 2016, 124 000 people died of tuberculosis in South Africa and the disease was declared the leading cause of death by Statistics South Africa. Continued efforts to use research to create a nation free of tuberculosis are underway.  Methods: A repeated measures investigation was performed with the aim of identifying the persistent predictors and the long-term patterns of tuberculosis infection in South Africa for the period 2008 to 2017. The most suitable Generalised Estimating Equations that describe the population average probability of infection over time were applied to a sample of respondents taken from the National Income Dynamics Survey data, wave 1 to wave 5. The response variable was binary with the outcome of interest being the respondents that self-reported to have been diagnosed with tuberculosis. To improve estimation efficiency, the best working correlation matrix for this data was selected.  Results: We used a sample of 8510 individuals followed for five waves, of these, 3.7%, 2.54%, 4.15%, 5.72% and 5.99% for waves 1, 2, 3, 4 and 5 respectively, reported to have been diagnosed with tuberculosis. Findings revealed that the independent working correlation matrix with the model-based standard error estimates gave the most robust results for the repeated measures tuberculosis data in South Africa. Furthermore, over the years, the average probability of being diagnosed with tuberculosis was positively associated with being single, male, middle-aged (30- 59 years), black African, unemployed, smoking, lower education levels, lack of regular exercise, asthma, suffering from other diseases, lack of access to improved sanitation, lower household income and expenditure.  Conclusion: The probabilities of tuberculosis infection are independent within individuals over time. The inequalities in socioeconomic status in South Africa caused the poor to be more at risk of tuberculosis over time from 2008 to 2017.&nbsp

Volatility Parameters Estimation and Forecasting of GARCH(1,1) Models with Johnson’s SU Distributed Errors

This paper proposes a GARCH-type model allowing for time-varying volatility, skewness, and kurtosis assuming a Johnson’s SU distribution for the error term. This distribution has two shape parameters and allows a wide range of skewness and kurtosis. We then impose dynamics on both shape parameters to obtain autoregressive conditional density (ARCD) models, allowing time-varying skewness and kurtosis. ARCD models with this distribution are applied to the daily returns of a variety of stock indices and exchange rates. Models with time-varying shape parameters are found to give better fit than models with constant shape parameters. Also, a weighted forecasting scheme is introduced to generate the sequence of the forecasts by computing a weighted average of the three alternative methods suggested in the literature. The results showed that the weighted average scheme did not show clear superiority to the other three methods

Spatio-Temporal Modelling of Tick Life-Stage Count Data With Spatially Varying Coefficients

There is a vast amount of geo-referenced data in many fields of study including ecological studies. Geo-referencing is usually by point referencing; that is, latitudes and longitudes or by areal referencing, which includes districts, counties, states, provinces and other administrative units. The availability of large geo-referenced datasets for modelling has necessitated the development and application of spatial statistical methods. However, spatial varying coefficients models exploring the abundance of tick counts remain limited. In this study we used data that was collected and prepared by researchers in the Department of Biological Sciences from the Old Dominion University, Virginia, USA. We modelled tick life-stage counts and abundance variability from 12 sampling locations, with 5 different habitats (numbered 1-5), three habitat types; namely: woods, edges and grass; collected monthly from May 2009 through December 2018. Spatio-temporal Poisson and spatio-temporal negative binomial (NB) count data models were fitted to the data and compared using the deviance information criteria (DIC). The NB model outperformed the Poisson models with all its DIC values being smaller than those of the Poisson model. Results showed that the covariates varied spatially across counties. There was a decreasing time (in years) effect over the study period. However, even though the time effect was decreasing over the study period, space-time interaction effects were seen to be increasing over time in York County

Forecasting financial variables using artificial neural networks - dynamic factor model

In this paper we introduce a new model that uses the dynamic factor model (DFM) framework combined with artificial neural network (ANN) analysis, which accommodates a large cross-section of financial and macroeconomic time series for forecasting. In our new ANN-DF model we use the factor model to extract factors from ANNs in sample forecasts for each single series of the dataset, which contains 228 monthly series. These factors are then used as explanatory variables in order to produce more accurate forecasts. We apply this new model to forecast three South African variables, namely, Rate on three-month trade financing, Lending rate and Short-term interest rate in the period 1992:1 to 2011:12. The model comparison results, based on the root mean square errors of three, six and twelve months ahead out-of-sample forecasts over the period 2007:1 to 2011:12 indicate that, in all of the cases, the ANN-DFM and the DFM statistically outperform the autoregressive (AR) models. In the majority of cases the ANN-DFM outperforms the DFM. The results indicate the usefulness of the factors in forecasting performance. The RMSE results are confirmed by the test of equality of forecast accuracy proposed by Diebold-Mariano

Determinants of out-of-pocket health expenditure and their welfare implications in a South African context

This study aims to quantify the extent of catastrophic household health expenditures on welfare and determine factors influencing it. A logistic regression model based on the logit link function was used to predict the probability of catastrophic health expenditure occurrence. A comparison between 2008 and 2012 health status of adults shows that there was a sizable improvement of the health status of individuals. The high level of catastrophic health expenditure may be associated with the low share of prepayment in national health expenditure, adequate availability of services and a high level of poverty which for South Africa is 46.2% according to the Statistics South Africa report (2015). Major factors determining the catastrophic expenditure besides poverty were spending on hospitalisation and medical supplies. Thus, reducing catastrophic expenditures requires an increase in financial protection offered to the poor through expanding government-financed benefits for the poor such as implementation of the Social Health Insurance (SHI) scheme, which will cover all poor households

An Integrated RNA and DNA Molecular Signature for Colorectal Cancer Classification

Paper presented at the 5th Strathmore International Mathematics Conference (SIMC 2019), 12 - 16 August 2019, Strathmore University, Nairobi, KenyaColorectal cancer (CRC) is the third most common cancer among women and men in the USA. The KRAS gene is mutated in 40% of the CRC cases and hence the RAS pathway activation has become a major focus of drug targeting efforts. However, nearly 60% of patients with wild-type KRAS fail to respond to RAS targeted therapies, for example the anti-epithelial growth factor receptor inhibitor (EGFRi) combination therapies. Thus, there is a need to develop more reliable molecular signatures to better predict mutation status. In this study, we develop a hybrid (DNA mutation and RNA expression) signature and assess its predictive properties for the mutation status of CRC patients. Publicly-available microarray and RNA-Seq data from 54 matched formalin-fixed paraffin embedded (FFPE) samples from the Affymetrix GeneChip and RNA-Seq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. For classification, the support-vector machines, artificial neural networks, random forests, k-nearest neighbors and the nave Bayes algorithms were employed. Compared to the genelist from each of the platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity and AUC for mutation status and could therefore be useful in clinical practice, especially for colorectal cancer diagnosis and therapeutics.University of KwaZulu-Natal, South Africa. University of South Carolina — Upstate, United States of America

Factors affecting child malnutrition in Ethiopia

Background: One of the public health problems in developing countries is child malnutrition. An important factor for children’s well-being is good nutrition. Therefore, the malnutrition status of children under the age of five is an important outcome measure for children’s health. This study uses the proportional odds model to identify risk factors associated with child malnutrition in Ethiopia using the 2016 Ethiopian Demographic and Health Survey data.Methods: This study uses the 2016 Ethiopian Demographic and Health Survey results. Based on weight-for-height anthropometric index (Z-score) child nutrition status is categorized into four levels namely- underweight, normal, overweight and obese. Since this leads to an ordinal variable for nutrition status, an ordinal logistic regression (OLR)proportional odds model (POM) is an obvious choice for analysis.Results: The findings and comparison of results using the cumulative logit model with and without complex survey design are presented. The study results revealed that to produce the appropriate estimates and standard errors for data that were obtained from complex survey design, model fitting based on taking the survey sampling design into account is better. It has also been found that for children under the age of five, weight of a child at birth, mother’s age, mother’s Body Mass Index (BMI), marital status of mother and region (Affar, Dire Dawa, Gambela, Harari and Somali) were influential variables significantly associated with underfive children’s nutritional status in Ethiopia.Conclusion: This child’s age of a child, sex, weight of child at birth, mother’s BMI and region of residence were significant determinants of malnutrition of children under five years in Ethiopia. The effect of these determinants can be used to develop strategies for reducing child malnutrition in Ethiopia. Moreover, these findings show that OLR proportional odds model is appropriate assessing thedeterminants of malnutrition for ordinal nutritional status of underfive children in Ethiopia.Keywords: BMI, Ethiopian Demographic and Health Survey (EDHS), malnutrition, proportional odds model