The wide application of the genomic microarray technology triggers a tremendous need in the development of the high dimensional genetic data analysis. Many statistical methods for the microarray data analysis consider one gene at a time, but they may miss subtle changes at the single gene level. This limitation may be overcome by considering a set of genes simultaneously where the gene sets are derived from the prior biological knowledge and are called "pathways". We have made contributions on two specific research topics related to the high dimensional genetic pathway data. One is to propose a semi- parametric model for identifying pathways related to the zero inflated clinical outcomes; the other is to propose a multilevel Gaussian graphical model for exploring both pathway and gene level network structures. For the first problem, we develop a semiparametric model via a Bayesian hierarchical framework. We model the pathway effect nonparametrically into a zero inflated Poisson hierarchical regression model with unknown link function. The nonparametric pathway effect is estimated via the kernel machine and the unknown link function is estimated by transforming a mixture of beta cumulative density functions. Our approach provides flexible semiparametric settings to describe the complicated association between gene microarray expressions and the clinical outcomes. The Metropolis-within-Gibbs sampling algorithm and Bayes factor are used to make the statistical inferences. Our simulation results support that the semiparametric approach is more accurate and flexible than the zero inflated Poisson regression with the canonical link function, this is especially true when the number of genes is large. The usefulness of our approaches is demonstrated through its applications to a canine gene expression data set (Enerson et al., 2006). Our approaches can also be applied to other settings where a large number of highly correlated predictors are present. Unlike the first problem, the second one is to take into account that pathways are not independent of each other because of shared genes and interactions among pathways. Multi-pathway analysis has been a challenging problem because of the complex dependence structure among pathways. By considering the dependency among pathways as well as genes within each pathway, we propose a multi-level Gaussian graphical model (MGGM): one level is for pathway network and the second one is for gene network. We develop a multilevel L1 penalized likelihood approach to achieve the sparseness on both levels. We also provide an iterative weighted graphical LASSO algorithm (Guo et al., 2011) for MGGM. Some asymptotic properties of the estimator are also illustrated. Our simulation results support the advantages of our approach; our method estimates the network more accurate on the pathway level, and sparser on the gene level. We also demonstrate usefulness of our approach using the canine genes-pathways data set. / Ph. D.

This dissertation investigates uncertainty quantification in dynamic problems. The Advanced Mean Value (AMV) method is used to calculate probabilistic sound power and the sensitivity of elastically supported panels with small uncertainty (coefficient of variation). Sound power calculations are done using Finite Element Method (FEM) and Boundary Element Method (BEM). The sensitivities of the sound power are calculated through direct differentiation of the FEM/BEM/AMV equations. The results are compared with Monte Carlo simulation (MCS). An improved method is developed using AMV, metamodel, and MCS. This new technique is applied to calculate sound power of a composite panel using FEM and Rayleigh Integral. The proposed methodology shows considerable improvement both in terms of accuracy and computational efficiency. In systems with large uncertainties, the above approach does not work. Two Spectral Stochastic Finite Element Method (SSFEM) algorithms are developed to solve stochastic eigenvalue problems using Polynomial chaos. Presently, the approaches are restricted to problems with real and distinct eigenvalues. In both the approaches, the system uncertainties are modeled by Wiener-Askey orthogonal polynomial functions. Galerkin projection is applied in the probability space to minimize the weighted residual of the error of the governing equation. First algorithm is based on inverse iteration method. A modification is suggested to calculate higher eigenvalues and eigenvectors. The above algorithm is applied to both discrete and continuous systems. In continuous systems, the uncertainties are modeled as Gaussian processes using Karhunen-Loeve (KL) expansion. Second algorithm is based on implicit polynomial iteration method. This algorithm is found to be more efficient when applied to discrete systems. However, the application of the algorithm to continuous systems results in ill-conditioned system matrices, which seriously limit its application. Lastly, an algorithm to find the basis random variables of KL expansion for non-Gaussian processes, is developed. The basis random variables are obtained via nonlinear transformation of marginal cumulative distribution function using standard deviation. Results are obtained for three known skewed distributions, Log-Normal, Beta, and Exponential. In all the cases, it is found that the proposed algorithm matches very well with the known solutions and can be applied to solve non-Gaussian process using SSFEM. / Ph. D.

Response of flexible structures — such as cable-supported bridges and aircraft wings — is associated with a number of uncertainties in structural and flow parameters. This thesis is aimed at efficient uncertainty quantification in a few such flow and flow-induced structural response problems. First, the uncertainty quantification in the lift force exerted on a submerged body in a potential flow is considered. To this end, a new method — termed here as semi-intrusive stochastic perturbation (SISP) — is proposed. A sensitivity analysis is also performed, where for the global sensitivity analysis (GSA) the Sobol’ indices are used. The polynomial chaos expansion (PCE) is used for estimating these indices. Next, two stability problems —divergence and flutter — in the aeroelasticity are studied in the context of reliability based design optimization (RBDO). Two modifications are proposed to an existing PCE-based metamodel to reduce the computational cost, where the chaos coefficients are estimated using Gauss quadrature to gain computational speed and GSA is used to create nonuniform grid to reduce the cost even further. The proposed method is applied on a rectangular unswept cantilever wing model. Next, reliability computation in limit cycle oscillations (LCOs) is considered. While the metamodel performs poorly in this case due to bimodality in the distribution, a new simulation-based scheme proposed to this end. Accordingly, first a reduced-order model (ROM) is used to identify the critical region in the random parameter space. Then the full-scale expensive model is run only over a this critical region. This is applied to the rectangular unswept cantilever wing with cubic and fifth order stiffness terms in its equation of motion. Next, the wind speed is modeled as a spatio-temporal process, and accordingly new representations of spatio-temporal random processes are proposed based on tensor decompositions of the covariance kernel. These are applied to three problems: a heat equation, a vibration, and a readily available covariance model for wind speed. Finally, to assimilate available field measurement data on wind speed and to predict based on this assimilation, a new framework based on the tensor decompositions is proposed. The framework is successfully applied to a set of measured data on wind speed in Ireland, where the prediction based on simulation is found to be consistent with the observed data.

<p>Proučaveni su uop&scaron;teni slučajni procesi na različitim prostorima uop&scaron;tenih funkcija i date njihove reprezentacije. Dati su potrebni i dovoljni uslovi za različite konvergencije niza uop&scaron;tenih slučajnih procesa. Data je ekstenzija Gausovog uop&scaron;tenog slučajnog procesa na Hilbertovom prostoru.</p> / <p>Generalized random processes on different&nbsp; spaces of generalized functions are considered. The representation of such processes are given. Necessary and sufficient conditions for different convergence of a a sequence of generalized random pocesses are obtained. Extension of a Gaussian generalized random pocess to a Hilbert space is obtained.</p>

The subject of this thesis is the generation of spatial temporal structures in living cells. Specifically, we studied the Min-system in the bacterium Escherichia coli. It consists of the MinC, the MinD, and the MinE proteins, which play an important role in the correct selection of the cell division site. The Min-proteins oscillate between the two cell poles and thereby prevent division at these locations. In this way, E. coli divides at the center, producing two daughter cells of equal size, providing them with the complete genetic patrimony. Our goal is to perform a quantitative study, both theoretical and experimental, in order to reveal the mechanism underlying the Min-oscillations. Experimentally, we characterize theMin-system, measuring the temporal period of the oscillations as a function of the cell length, the time-averaged protein distributions, and the in vivo Min-protein mobility by means of different fluorescence microscopy techniques. Theoretically, we discuss a deterministic description based on the exchange of Minproteins between the cytoplasm and the cytoplasmic membrane and on the aggregation current induced by the interaction between membrane-bound proteins. Oscillatory solutions appear via a dynamic instability of the homogenous protein distributions. Moreover, we perform stochastic simulations based on a microscopic description, whereby the probability for each event is calculated according to the corresponding probability in the master equation. Starting from this microscopic description, we derive Langevin equations for the fluctuating protein densities which correspond to the deterministic equations in the limit of vanishing noise. Stochastic simulations justify this deterministic model, showing that oscillations are resistant to the perturbations induced by the stochastic reactions and diffusion. Predictions and assumptions of our theoretical model are compatible with our experimental findings. Altogether, these results enable us to propose further experiments in order to quantitatively compare the different models proposed so far and to test our model with even higher precision. They also point to the necessity of performing such an analysis through single cell measurements.

Model and variable selection have attracted considerable attention in areas of application where datasets usually contain thousands of variables. Variable selection is a critical step to reduce the dimension of high dimensional data by eliminating irrelevant variables. The general objective of variable selection is not only to obtain a set of cost-effective predictors selected but also to improve prediction and prediction variance. We have made several contributions to this issue through a range of advanced topics: providing a graphical view of Bayesian Variable Selection (BVS), recovering sparsity in multivariate nonparametric models and proposing a testing procedure for evaluating nonlinear interaction effect in a semiparametric model. To address the first topic, we propose a new Bayesian variable selection approach via the graphical model and the Ising model, which we refer to the ``Bayesian Ising Graphical Model'' (BIGM). There are several advantages of our BIGM: it is easy to (1) employ the single-site updating and cluster updating algorithm, both of which are suitable for problems with small sample sizes and a larger number of variables, (2) extend this approach to nonparametric regression models, and (3) incorporate graphical prior information. In the second topic, we propose a Nonnegative Garrote on a Kernel machine (NGK) to recover sparsity of input variables in smoothing functions. We model the smoothing function by a least squares kernel machine and construct a nonnegative garrote on the kernel model as the function of the similarity matrix. An efficient coordinate descent/backfitting algorithm is developed. The third topic involves a specific genetic pathway dataset in which the pathways interact with the environmental variables. We propose a semiparametric method to model the pathway-environment interaction. We then employ a restricted likelihood ratio test and a score test to evaluate the main pathway effect and the pathway-environment interaction. / Ph. D.

The subject of this thesis is the generation of spatial temporal structures in living cells. Specifically, we studied the Min-system in the bacterium Escherichia coli. It consists of the MinC, the MinD, and the MinE proteins, which play an important role in the correct selection of the cell division site. The Min-proteins oscillate between the two cell poles and thereby prevent division at these locations. In this way, E. coli divides at the center, producing two daughter cells of equal size, providing them with the complete genetic patrimony. Our goal is to perform a quantitative study, both theoretical and experimental, in order to reveal the mechanism underlying the Min-oscillations. Experimentally, we characterize theMin-system, measuring the temporal period of the oscillations as a function of the cell length, the time-averaged protein distributions, and the in vivo Min-protein mobility by means of different fluorescence microscopy techniques. Theoretically, we discuss a deterministic description based on the exchange of Minproteins between the cytoplasm and the cytoplasmic membrane and on the aggregation current induced by the interaction between membrane-bound proteins. Oscillatory solutions appear via a dynamic instability of the homogenous protein distributions. Moreover, we perform stochastic simulations based on a microscopic description, whereby the probability for each event is calculated according to the corresponding probability in the master equation. Starting from this microscopic description, we derive Langevin equations for the fluctuating protein densities which correspond to the deterministic equations in the limit of vanishing noise. Stochastic simulations justify this deterministic model, showing that oscillations are resistant to the perturbations induced by the stochastic reactions and diffusion. Predictions and assumptions of our theoretical model are compatible with our experimental findings. Altogether, these results enable us to propose further experiments in order to quantitatively compare the different models proposed so far and to test our model with even higher precision. They also point to the necessity of performing such an analysis through single cell measurements.