The reliability of networking and communication systems is vital for the nation's economy and security. Optical and cellular networks have become a critical infrastructure and are indispensable in emergency situations. This dissertation outlines methods for analyzing such infrastructures in the presence of catastrophic failures, such as a hurricane, as well as accidental failures of one or more components. Additionally, it presents a method for protecting against the loss of a single link in a multicast network along with a technique that enables wireless clients to efficiently recover lost data sent by their source through collaborative information exchange. Analysis of a network's reliability during a natural disaster can be assessed by simulating the conditions in which it is expected to perform. This dissertation conducts the analysis of a cellular infrastructure in the aftermath of a hurricane through Monte-Carlo sampling and presents alternative topologies which reduce resulting loss of calls. While previous research on restoration mechanisms for large-scale networks has mostly focused on handling the failures of single network elements, this dissertation examines the sampling methods used for simulating multiple failures. We present a quick method of nding a lower bound on a network's data loss through enumeration of possible cuts as well as an efficient method of nding a tighter lower bound through genetic algorithms leveraging the niching technique. Mitigation of data losses in a multicast network can be achieved by adding redundancy and employing advanced coding techniques. By using Maximum Rank Distance (MRD) codes at the source, a provider can create a parity packet which is e ectively linearly independent from the source packets such that all packets may be transmitted through the network using the network coding technique. This allows all sinks to recover all of the original data even with the failure of an edge within the network. Furthermore, this dissertation presents a method that allows a group of wireless clients to cooperatively recover from erasures (e.g., due to failures) by using the index coding techniques.

A sign pattern matrix is a matrix whose entries are from the set {+,-,0}. The minimum rank of a sign pattern matrix A is the minimum of the rank of the real matrices whose entries have signs equal to the corresponding entries of A. It is conjectured that the minimum rank of every sign pattern matrix can be realized by a rational matrix. The equivalence of this conjecture to several seemingly unrelated statements are established. For some special cases, such as when A is entrywise nonzero, or the minimum rank of A is at most 2, or the minimum rank of A is at least n - 1,(where A is mxn), the conjecture is shown to hold.Connections between this conjecture and the existence of positive rational solutions of certain systems of homogeneous quadratic polynomial equations with each coefficient equal to either -1 or 1 are explored. Sign patterns that almost require unique rank are also investigated.

A sign pattern matrix is a matrix whose entries are from the set {+,-, 0}. For a real matrix B, sgn(B) is the sign pattern matrix obtained by replacing each positive respectively, negative, zero) entry of B by + (respectively, -, 0). For a sign pattern matrixA, the sign pattern class of A, denoted Q(A), is defined as { B : sgn(B)= A }. The minimum rank mr(A)(maximum rank MR(A)) of a sign pattern matrix A is the minimum (maximum) of the ranks of the real matrices in Q(A). Several results concerning sign patterns A that require almost unique rank, that is to say, the sign patterns A such that MR(A)= mr(A)+1 are established. In particular, a complete characterization of these sign patterns is obtained. Further, the results on sign patterns that require almost unique rank are extended to sign patterns A for which the spread is d =MR(A)-mr(A).

This dissertation is organized into three independent chapters. In Chapter 1, I consider the selection of weights for averaging a set of threshold models. Existing model averaging literature primarily focuses on averaging linear models, I consider threshold regression models. The theory I developed in that chapter demonstrates that the proposed jackknife model averaging estimator achieves asymptotic optimality when the set of candidate models are all misspecified threshold models. The simulations study demonstrates that the jackknife model averaging estimator achieves the lowest mean squared error when contrasted against other model selection and model averaging methods. In Chapter 2, I propose a model averaging framework for the synthetic control method of Abadie and Gardeazabal (2003) and Abadie et al. (2010). The proposed estimator serves a twofold purpose. First, it reduces the bias in estimating the weights each member of the donor pool receives. Secondly, it accounts for model uncertainty for the program evaluation estimation. I study two variations of the model, one where model weights are derived by solving a cross-validation quadratic program and another where each candidate model receives equal weights. Next, I show how to apply the placebo study and the conformal inference procedure for both versions of my estimator. With a simulation study, I reveal that the superior performance of the proposed procedure. In Chapter 3, which is co-authored with my advisor Professor Youngki Shin, we provide an exact computation algorithm for the maximum rank correlation estimator using the mixed integer programming (MIP) approach. We construct a new constrained optimization problem by transforming all indicator functions into binary parameters to be estimated and show that the transformation is equivalent to the original problem. Using a modern MIP solver, we apply the proposed method to an empirical example and Monte Carlo simulations. The results show that the proposed algorithm performs better than the existing alternatives. / Dissertation / Doctor of Philosophy (PhD)