Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Statistics and Biostatistics." Includes bibliographical references (p. 53-55).

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Computational Biology and Molecular Biophysics." Includes bibliographical references (p. 210-226).

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 132-137). Also available on the Internet.

published_or_final_version / Computer Science / Master / Master of Philosophy

In downlink multi-user multiple-input multiple-output (MU-MIMO) systems, different users, even multiple data streams serving one user, might require different quality-of-services (QoS). The transceiver should allocate resources to different users aiming at satisfying their QoS requirements. In order to design the optimal transceiver, channel state information is necessary. In practice, channel state information has to to be estimated, and estimation error is unavoidable. Therefore, robust transceiver design, which takes the channel estimation uncertainty into consideration, is important. For the previous robust transceiver designs, bounded estimation errors or Gaussian estimation errors were assumed. However, if there exists unknown distributed interference, the distribution of the channel estimation error cannot be modeled accurately a priori. Therefore, in this thesis, we investigate the robust transceiver design problem in downlink MU-MIMO system under probabilistic QoS constraints with arbitrary distributed channel estimation error. To tackle the probabilistic QoS constraints under arbitrary distributed channel estimation error, the transceiver design problem is expressed in terms of worst-case probabilistic constraints. Two methods are then proposed to solve the worst-case problem. Firstly, the Chebyshev inequality based method is proposed. After the worst-case probabilistic constraint is approximated by the Chebyshev inequality, an iteration between two convex subproblems is proposed to solve the approximated problem. The convergence of the iterative method is proved, the implementation issues and the computational complexity are discussed. Secondly, in order to solve the worst-case probabilistic constraint more accurately, a novel duality method is proposed. After a series of reformulations based on duality and S-Lemma, the worst-case statistically constrained problem is transformed into a deterministic finite constrained problem, with strong duality guaranteed. The resulting problem is then solved by a convergence-guaranteed iteration between two subproblems. Although one of the subproblems is still nonconvex, it can be solved by a tight semidefinite relaxation (SDR). Simulation results show that, compared to the non-robust method, the QoS requirement is satisfied by both proposed algorithms. Furthermore, among the two proposed methods, the duality method shows a superior performance in transmit power, while the Chebyshev method demonstrates a lower computational complexity. / published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

published_or_final_version / abstract / toc / Statistics and Actuarial Science / Master / Master of Philosophy

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No description available.

Extreme rainfall events may have catastrophic impacts on the population and infrastructures, therefore it is essential to have accurate knowledge of extreme rainfall characteristics. Moreover, both the scientific community and policymakers have recently shown a growing interest in the potential impacts of climate change on water resources management. Indeed, changes in the intensity and frequency of occurrence of extreme rainfall events may have serious impacts. As such, it is important to understand not only the current patterns of extreme rainfalls but also how they are likely to change in the future. / The objective of the present research is therefore to find the best method for estimating accurately extreme rainfalls for the current time period and future periods in the context of climate change. The analysis of extreme rainfall data from the province of Quebec (Canada) revealed that, according to L-moment ratio diagrams, the data may be well described by the Generalized-Extreme-Value (GEV) distribution. Results also showed that a simple scaling relationship between non-central moments (NCM) and duration can be established and that a scaling method based on NCMs and scaling exponents can be used to generate accurate estimates of extreme rainfalls at Dorval station (Quebec, Canada). Other results demonstrated that the method of NCMs can accurately estimate distribution parameters and can be used to construct accurate Intensity-Duration-Frequency (IDF) curves. / Furthermore, a regional analysis was performed and homogenous regions of weather stations within Quebec were identified. A method for the estimation of missing data at ungauged sites based on regional NCMs was found to yield good estimates. / In addition, the potential impacts of climate change on extreme rainfalls were assessed. Changes in the distribution of annual maximum (AM) precipitations were evaluated using simulations from two Global Climate Models (GCMs) under the A2 greenhouse gas emission scenario: the Coupled Global Climate Model version 2 (CGCM2A2) of the Canadian Centre for Climate Modelling and Analysis, and the Hadley Centre's Model version 3 (HadCM3A2). Simulations from these two models were downscaled spatially using the Statistical DownScaling Model (SDSM). A bias-correction method to adjust the downscaled AM daily precipitations for Dorval station was tested and results showed that after adjustments, the values fit the observed AM daily precipitations well. The analysis of future AM precipitations revealed that, after adjustments, AM precipitations downscaled from CGCM2A2 increase from current to future periods, while AM precipitations downscaled from HadCM3A2 show a mild decrease from current to future periods, for daily and sub-daily scales.

The transmission of malaria is among the leading public health problems in Ethiopia. From the total area of Ethiopia, more than 75% is malarious. Identifying the infectiousness of malaria by socio-economic, demographic and geographic risk factors based on the malaria rapid diagnosis test (RDT) survey results has several advantages for planning, monitoring and controlling, and eventual malaria eradication effort. Such a study requires thorough understanding of the diseases process and associated factors. However such studies are limited. Therefore, the aim of this study was to use different statistical tools suitable to identify socioeconomic, demographic and geographic risk factors of malaria based on the malaria rapid diagnosis test (RDT) survey results in Ethiopia. A total of 224 clusters of about 25 households were selected from the Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia. Accordingly, a number of binary response statistical analysis models were used. Multiple correspondence analysis was carried out to identify the association among socioeconomic, demographic and geographic factors. Moreover a number of binary response models such as survey logistic, GLMM, GLMM with spatial correlation, joint models and semi-parametric models were applied. To test and investigate how well the observed malaria RDT result, use of mosquito nets and use of indoor residual spray data fit the expectations of the model, Rasch model was used. The fitted models have their own strengths and weaknesses. Application of these models was carried out by analysing data on malaria RDT result. The data used in this study, which was conducted from December 2006 to January 2007 by The Carter Center, is from baseline malaria indicator survey in Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia. The correspondence analysis and survey logistic regression model was used to identify predictors which affect malaria RDT results. The effect of identified socioeconomic, demographic and geographic factors were subsequently explored by fitting a generalized linear mixed model (GLMM), i.e., to assess the covariance structures of the random components (to assess the association structure of the data). To examine whether the data displayed any spatial autocorrelation, i.e., whether surveys that are near in space have malaria prevalence or incidence that is similar to the surveys that are far apart, spatial statistics analysis was performed. This was done by introducing spatial autocorrelation structure in GLMM. Moreover, the customary two variables joint modelling approach was extended to three variables joint effect by exploring the joint effect of malaria RDT result, use of mosquito nets and indoor residual spray in the last twelve months. Assessing the association between these outcomes was also of interest. Furthermore, the relationships between the response and some confounding covariates may have unknown functional form. This led to proposing the use of semiparametric additive models which are less restrictive in their specification. Therefore, generalized additive mixed models were used to model the effect of age, family size, number of rooms per person, number of nets per person, altitude and number of months the room sprayed nonparametrically. The result from the study suggests that with the correct use of mosquito nets, indoor residual spraying and other preventative measures, coupled with factors such as the number of rooms in a house, are associated with a decrease in the incidence of malaria as determined by the RDT. However, the study also suggests that the poor are less likely to use these preventative measures to effectively counteract the spread of malaria. In order to determine whether or not the limited number of respondents had undue influence on the malaria RDT result, a Rasch model was used. The result shows that none of the responses had such influences. Therefore, application of the Rasch model has supported the viability of the total sixteen (socio-economic, demographic and geographic) items for measuring malaria RDT result, use of indoor residual spray and use of mosquito nets. From the analysis it can be seen that the scale shows high reliability. Hence, the result from Rasch model supports the analysis carried out in previous models. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.