Lesotho, like any other country of the world, is faced with the task of improving the / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2007.

As genomic sequencing technologies continue to advance, researchers are furthering their understanding of the relationships between genetic variants and expressed traits (Hirschhorn and Daly, 2005). However, missing data can significantly limit the power of a genetic study. Here, the use of a regularized generalized linear model, denoted GLMNET is proposed to impute missing genotypes. The method aimed to address certain limitations of earlier regression approaches in regards to genotype imputation, particularly multicollinearity among predictors. The performance of GLMNET-based method is compared to the performance of the phase-based method fastPHASE. Two simulation settings were evaluated: a sparse-missing model, and a small-panel expan- sion model. The sparse-missing model simulated a scenario where SNPs were missing in a random fashion across the genome. In the small-panel expansion model, a set of test individuals that were only genotyped at a small subset of the SNPs of the large panel. Each imputation method was tested in the context of two data-sets: Canadian Holstein cattle data and human HapMap CEU data. Although the proposed method was able to perform with high accuracy (>90% in all simulations), fastPHASE per- formed with higher accuracy (>94%). However, the new method, which was coded in R, was able to impute genotypes with better time efficiency than fastPHASE and this could be further improved by optimizing in a compiled language.

Malawi’s economy relies heavily on agriculture which is threatened by declines in soil fertility. Measures to ensure increased crop productivity at household level include the increased use of inorganic fertilizers. To supplement the Government’s effort in ensuring food security, Rural Livelihood Diversification Project (RLDP) was implemented in Kasungu and Lilongwe Districts in Malawi. The RLDP Project was aimed at increasing accessibility and utilisation of inorganic fertilizers. We used the data collected by the International Center for Tropical Agriculture (CIAT), to investigate if there could be any significant impacts of the interventions carried out by the project. A general linear model was initially used to model the data. Terms in the model were selected using the automatic stepwise procedure in GLMSELECT procedure of SAS. Other models that were used included a transformed response general linear model, gamma model based on log link and its alternative inverse link, and quantile regression procedures were used in modelling the amount of fertilizer use per acre response given a set of fixed effect predictors where households were only sampled at baseline or impact assessment study. The general linear model failed to comply with the model assumption of normality and constant variance. The gamma model was affected by influential observations. Quantile regression model is robust to outliers and influential observations. Quantile regression provided that number of plots cultivated, timeline, household saving and irrigation interaction, and the interaction between plots and timeline significantly affected the amounts of fertilizers applied per acre amongst the 25% of the households who apply lower levels of fertilizer per acre. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

Just over half (50:6%) of the population of South Sudan lives on less than one US Dollar a day. Three quarters of the population live below the poverty line (NBS, Poverty Report, 2010). Generally, effective government policy to reduce unemployment and eradicate poverty focuses on stimulating new businesses. Micro and small enterprises (MSEs) are the major source of employment and income for many in under-developed countries. The objective of this study is to identify factors that determine business success and failure in South Sudan. To achieve this objective, generalized linear models, survey logistic models, the generalized linear mixed models and multiple correspondence analysis are used. The data used in this study is generated from the business survey conducted in 2010. The response variable, which is defined as business success or failure was measured by profit and loss in businesses. Fourteen explanatory variables were identified as factors contributing to business success and failure. A main effect model consisting of the fourteen explanatory variables and three interaction effects were fitted to the data. In order to account for the complexity of the survey design, survey logistic and generalized linear mixed models are refitted to the same variables in the main effect model. To confirm the results from the model we used multiple correspondence analysis. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.

The analysis of longitudinal binary data can be undertaken using any of the three families of models namely, marginal, random effects and conditional models. Each family of models has its own respective merits and demerits. The models are applied in the analysis of binary longitudinal data for childhood disease data namely the Respiratory Syncytial Virus (RSV) data collected from a study in Kilifi, coastal Kenya. The marginal model was fitted using generalized estimating equations (GEE). The random effects models were fitted using ‘Proc GLIMMIX’ and ‘NLMIXED’ in SAS and then again in Genstat. Because the data is a state transition type of data with the Markovian property the conditional model was used to capture the dependence of the current response to the previous response which is known as the history. The data set has two main complicating issues. Firstly, there is the question of developing a stochastically based probability model for the disease process. In the current work we use direct likelihood and generalized linear modelling (GLM) approaches to estimate important disease parameters. The force of infection and the recovery rate are the key parameters of interest. The findings of the current work are consistent and in agreement with those in White et al. (2003). The aspect of time dependence on the RSV disease is also highlighted in the thesis by fitting monthly piecewise models for both parameters. Secondly, there is the issue of incomplete data in the analysis of longitudinal data. Commonly used methods to analyze incomplete longitudinal data include the well known available case analysis (AC) and last observation carried forward (LOCF). However, these methods rely on strong assumptions such as missing completely at random (MCAR) for AC analysis and unchanging profile after dropout for LOCF analysis. Such assumptions are too strong to generally hold. In recent years, methods of analyzing incomplete longitudinal data have become available with weaker assumptions, such as missing at random (MAR). Thus we make use of multiple imputation via chained equations that require the MAR assumption and maximum likelihood methods that result in the missing data mechanism becoming ignorable as soon as it is MAR. Thus we are faced with the problem of incomplete repeated non–normal data suggesting the use of at least the Generalized Linear Mixed Model (GLMM) to account for natural individual heterogeneity. The comparison of the parameter estimates using the different methods to handle the dropout is strongly emphasized in order to evaluate the advantages of the different methods and approaches. The survival analysis approach was also utilized to model the data due to the presence of multiple events per subject and the time between these events. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermarizburg, 2008.

In the present paper we discuss Bayesian estimation of a very general model class where the distribution of the observations is assumed to depend on a latent mixture or switching variable taking values in a discrete state space. This model class covers e.g. finite mixture modelling, Markov switching autoregressive modelling and dynamic linear models with switching. Joint Bayesian estimation of all latent variables, model parameters and parameters determining the probability law of the switching variable is carried out by a new Markov Chain Monte Carlo method called permutation sampling. Estimation of switching and mixture models is known to be faced with identifiability problems as switching and mixture are identifiable only up to permutations of the indices of the states. For a Bayesian analysis the posterior has to be constrained in such a way that identifiablity constraints are fulfilled. The permutation sampler is designed to sample efficiently from the constrained posterior, by first sampling from the unconstrained posterior - which often can be done in a convenient multimove manner - and then by applying a suitable permutation, if the identifiability constraint is violated. We present simple conditions on the prior which ensure that this method is a valid Markov Chain Monte Carlo method (that is invariance, irreducibility and aperiodicity hold). Three case studies are presented, including finite mixture modelling of fetal lamb data, Markov switching Autoregressive modelling of the U.S. quarterly real GDP data, and modelling the U .S./U.K. real exchange rate by a dynamic linear model with Markov switching heteroscedasticity. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Environmental factors affect fish distribution and fisher behavior. These factors are seldom included in stock assessment models, resulting in potentially inaccurate fish abundance estimates. This study determined the impact of these factors using the commercial catch rate of sauger (Sander canadensis) and walleye (Sander vitreus) in Lake Winnipeg by: (1) the use of satellite data to monitor turbidity and its impact on catch via simple linear regression and (2) the effect of environment on catch and effort using generalized linear models. No statistically significant relationship was found between catch and turbidity; a result which may be due to small sample sizes, the fish species' examined, and variable turbidity at depth. Decreased effort was correlated with harsh weather and decreased walleye catch. Increased walleye catch was correlated with low temperature and low Red River discharge. Increased sauger catch was correlated with high temperature, high cloud opacity, and average Red River discharge.

Environmental factors affect fish distribution and fisher behavior. These factors are seldom included in stock assessment models, resulting in potentially inaccurate fish abundance estimates. This study determined the impact of these factors using the commercial catch rate of sauger (Sander canadensis) and walleye (Sander vitreus) in Lake Winnipeg by: (1) the use of satellite data to monitor turbidity and its impact on catch via simple linear regression and (2) the effect of environment on catch and effort using generalized linear models. No statistically significant relationship was found between catch and turbidity; a result which may be due to small sample sizes, the fish species' examined, and variable turbidity at depth. Decreased effort was correlated with harsh weather and decreased walleye catch. Increased walleye catch was correlated with low temperature and low Red River discharge. Increased sauger catch was correlated with high temperature, high cloud opacity, and average Red River discharge.

Licentiatavhandling Uppsala : Sveriges lantbruksuniversitet, 2006. / Härtill 2 uppsatser.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniversitet, 2006. / Härtill 4 uppsatser.