Mobile telecommunications industry has experienced high growth rates for the recent 30 years. Accordingly, forecasting the future of mobile telecommunications services is important not only for mobile operators but also for all stakeholders in this industry ranging from handset manufacturers to vendors. In this thesis, the diffusion of mobile telecommunications services in 20 countries from different regions around the world is examined for the period of 1981 to 2010 with special emphasis on Turkey, in order to address the uncertainty in optimal model selection. The Gompertz, logistic and Bass models are fitted to the observed data of mobile phone penetration by means of nonlinear least squares. The fitness accuracies of the models are evaluated based on root mean square error (RMSE). Empirical results show that S-shaped growth models are capable of explaining the diffusion of mobile telecommunications services. The findings also suggest that there is no superior model in defining the diffusion process and the most suitable model is country dependent. Finally, we observe that the diffusion in late entrant countries appears to be faster than pioneer countries and peak demands in mobile telephones occur during the period of 1999 to 2006, which suggests a remarkable multinational learning effect and significance of the transition into digital technology.

Reinsurance is one of the most important tools to be used by insurance companies, for managing risks. This is an effective way / however, there are situations where reinsurance is insufficient, such as the occurrence of a natural hazard. When a natural hazard occurs, many insured experience loss at the same time, which drains the reinsurance market capacity. If future market capacity could be forecasted, then it would be easier for companies to decide when to include cat bonds or any other additional securities in their portfolio. In order to establish a model for market capacity, its relationship with other market parameters and the association among parameters are examined. In this study, these relationships are analyzed and used to establish an algorithm for predicting the next years reinsurance capacity. Moreover, last 10-year data for market capacity is used to establish and AR(1) model, in order to create a comparison with the algorithm. A case study of cat bonds is done, which uses the pricing load calculation of the Lane model and aims to ease the decision-making process by comparing the loads of cat bond and reinsurance pricing.

In this thesis we obtain several interesting algebraic properties of the operations used in the block cipher IDEA which are important for cryptographic analyzes. We view each of these operations as a function from $mathbb Z_{2}^n times mathbb Z_{2}^n to mathbb Z_{2}^n$. By fixing one of variables $v(z)=mathbf Z$ in $mathbb Z_{2}^n times mathbb Z_{2}^n$, we define functions $mathbf {f}_z$ and $mathbf {g}_z$ from $mathbb Z_{2}^n$ to $mathbb Z_{2}^n$ for the addition $BIGboxplus$ and the multiplication $BIGodot$ operations, respectively. We first show that the nonlinearity of $mathbf {g}_z$ remains the same under some transformations of $z$. We give an upper bound for the nonlinearity of $mathbf {g}_{2^k}$, where $2leq k &lt / n-1$. We list all linear relations which make the nonlinearity of $mathbf {f}_z$ and $mathbf {g}_z$ zero and furthermore, we present all linear relations for $mathbf {g}_z$ having a high probability. We use these linear relations to derive many more linear relations for 1-round IDEA. We also devise also a new algorithm to find a set of new linear relations for 1-round IDEA based on known linear relations. Moreover, we extend the largest known linear class of weak keys with cardinality $2^{23}$ to two classes with cardinality $2^{24}$ and $2^{27}$. Finally, we obtain several interesting properties of the set $ { ({mathbf X},{mathbf X} BIGoplus {mathbf A}) in mathbb Z_2^n times mathbb Z_2^n ,|, (mathbf {X}BJoin {mathbf Z})BIGoplus( ({mathbf X} BIGoplus {mathbf A} ) BJoin mathbf {Z} ) = {mathbf B} }$ for varying ${mathbf A}, {mathbf B}$ and ${mathbf Z}$ in $mathbb Z_2^n$, where $BJoin in { BIGodot,BIGboxplus }$. By using some of these properties, we present impossible differentials for 1-round IDEA and Pseudo-Hadamard Transform.

Efficient management of blood throughout the countries is of great economic importance, in addition to its major impact on the success of medical operations. This study is concerned with the analysis of policies for managing a unique blood supply chain network, which is defined in the new Blood and Blood Products Law of the Republic of Turkey. The main objective of the study is to obtain a better understanding of the system, and to find improved policies to be able to manage it efficiently. A discrete event simulation model is developed to analyze the blood supply chain of a pilot region in Turkey. Effects of different management policies on the supply chain performance are analyzed. Important improvements are achieved in terms of the selected performance measures such as outdate, mismatch and shortage rates of the region. Our proposed model can be used by both national health authorities and the Turkish Red Crescent Society as a decision support tool to analyze other regions and to examine alternative policies.

In this thesis, we address the route selection problem for Unmanned Air Vehicles (UAV) under multiple objectives. We consider a general case for this problem where the UAV has to visit several targets and return to the base. For this case, there are multiple combinatorial problems to be considered. First, the paths to be followed between any pairs of targets should be determined. This part can be considered as a multi-objective shortest path problem. Additionally, we need to determine the order of the targets to be visited. This in turn, is a multi-objective traveling salesperson problem. The overall problem is a combination of these two combinatorial problems. The route selection for UAVs has been studied by several researchers, mainly in the military context. They considered a linear combination of the two objectives / minimizing distance traveled and minimizing radar detection threat / and proposed heuristics for the minimization of the composite single objective problem. We treat these two objectives separately. We develop an evolutionary algorithm to determine the efficient tours. We also consider an exact interactive approach to identify the best paths and tours of a decision maker. We tested the two solution approaches on both small-sized and large-sized problem instances.

There is a growing interest in electronic auctions in the literature. Many researchers work on the single attribute version of the problem. Multi-attribute version of the problem is more realistic. However, this brings a substantial difficulty in solving the problem. In order to overcome the computational difficulties, we develop an Evolutionary Algorithm (EA) for the case of multi-attribute multi-item reverse auctions. We generate the whole Pareto front using the EA. We also develop heuristic procedures to find several good initial solutions and insert those in the initial population of the EA. We test the EA on a number of randomly generated problems and compare the results with the true Pareto optimal front obtained by solving a series of integer programs. We also develop an exact interactive approach that provides aid both to the buyer and the sellers for a multi-attribute single item multi round reverse auction. The buyer decides on the provisional winner at each round. Then the approach provides support in terms of all attributes to each seller to be competitive in the next round of the auction.

Inflation-indexed instruments provide a real return and protect investors from the erosion of the purchasing power of money. Hence, inflation-indexed markets grow very fast day by day. In this thesis, we focus on pricing of the inflation-indexed swaps and swaptions which are the most liquid derivative products traded in the inflation-indexed markets. Firstly, we review the Hull-White extended Vasicek model in the HJM framework. Then, we use this model to price inflation-indexed swaps. Also, pricing of inflation-indexed swaptions is given using Black&rsquo / s market model.

For statistical estimation of population parameters, Fisher&rsquo / s maximum likelihood estimators (MLEs) are commonly used. They are consistent, unbiased and efficient, at any rate for large n. In most situations, however, MLEs are elusive because of computational difficulties. To alleviate these difficulties, Tiku&rsquo / s modified maximum likelihood estimators (MMLEs) are used. They are explicit functions of sample observations and easy to compute. They are asymptotically equivalent to MLEs and, for small n, are equally efficient. Moreover, MLEs and MMLEs are numerically very close to one another. For calculating MLEs and MMLEs, the functional form of the underlying distribution has to be known. For machine data processing, however, such is not the case. Instead, what is reasonable to assume for machine data processing is that the underlying distribution is a member of a broad class of distributions. Huber assumed that the underlying distribution is long-tailed symmetric and developed the so called M-estimators. It is very desirable for an estimator to be robust and have bounded influence function. M-estimators, however, implicitly censor certain sample observations which most practitioners do not appreciate. Tiku and Surucu suggested a modification to Tiku&rsquo / s MMLEs. The new MMLEs are robust and have bounded influence functions. In fact, these new estimators are overall more efficient than M-estimators for long-tailed symmetric distributions. In this thesis, we have proposed a new modification to MMLEs. The resulting estimators are robust and have bounded influence functions. We have also shown that they can be used not only for long-tailed symmetric distributions but for skew distributions as well. We have used the proposed modification in the context of experimental design and linear regression. We have shown that the resulting estimators and the hypothesis testing procedures based on them are indeed superior to earlier such estimators and tests.

Privacy of personal health records is of ultimate importance. Unfortunately, it is easy to obtain illegal access to electronic health records under insufficient security precautions. Access control based on token or username/password is not adequate for applications in health domain which require heightened security. Currently, electronic signature mechanisms are being employed as a strong alternative to classic methods. In addition, biometrics provide more precise results in comparison to electronic signature methods. However, applicability of biometrics in this field has been prohibited by factors such as the need for special hardware, increased implementation costs, and invasiveness of the biometry sensors (eg. iris topology, fingerprint). Behavioral biometrics such as speech, and keystroke dynamics are easier to implement, and do not suffer from the disadvantages mentioned for the static biometrics. Especially, using keystroke dynamics for user authentication is more advantageous than other advanced biometrics because the implementation is inexpensive and continuous identity control is plausible. The aim of this study is to show the feasibility of merging a biometry-based advanced identity verification method together with an initial access control procedure such as password check. In this study, we provide an authentication framework based on measuring similarity of the typing characteristics of medical reporters, while they are typing medical reports. We have made a prototype of the system and provided classification of keystroke timings for each operator. We have generated a testbed and measured similarity of typing patterns of 5 medical reporters upon typing 4 different kinds of medical reports. Our system performs with hundred percent accuracy in identifying the authorized operators from the reports they type. In current practice, electronic signatures are indispensable for health information systems, but our study shows that keystroke dynamics can easily be included in this chain for increased security.

This thesis is motivated by a construction company whose main business operation is foundation installations of offshore wind farm projects. Steel is the main input for most of the foundations. This construction company is confronted with price risk when involved in the procurement part of the foundations. The company receives a fixed payment for the project while paying variable raw material costs which depend on the steel spot price on the procurement date. The construction company should use financial markets to eliminate the price risk. However, in most wind farm projects, the steel requirement of a foundation is not known in advance, i.e., there is also a quantity risk. Therefore, it is not possible to completely eliminate the associated risk. In this thesis we analyze the hedging decisions of a value maximizing construction company confronted with both price and quantity risks under the presence of capital market imperfections.