In this thesis, a simulation study is held and a statistical model is fitted to the simulated data. This data is assumed to be the satisfaction of the customers who withdraw their salary from a particular bank. It is a longitudinal data which has bivariate and binary response. It is assumed to be collected from 200 individuals at four different time points. In such data sets, two types of dependence -the dependence within subject measurements and the dependence between responses- are important and these are considered in the model. The model is Marginalized Transition Random Effects Models, which has three levels. The first level measures the effect of covariates on responses, the second level accounts for temporal changes, and the third level measures the difference between individuals. Markov Chain Monte Carlo methods are used for the model fit. In the simulation study, the changes between the estimated values and true parameters are searched under two conditions, when the model is correctly specified or not. Results suggest that the better convergence is obtained with the full model. The third level which observes the individual changes is more sensitive to the model misspecification than the other levels of the model.

This study presents the theoretical analysis of an active flutter suppression methodology applied to a smart fin. The smart fin consists of a cantilever aluminum plate-like structure with surface bonded piezoelectric (PZT, Lead- Zirconate-Titanate) patches. A thermal analogy method for the purpose of modeling of piezoelectric actuators in MSC&reg / /NASTRAN based on the analogy between thermal strains and piezoelectric strains was presented. The results obtained by the thermal analogy were compared with the reference results and very good agreement was observed. The unsteady aerodynamic loads acting on the structure were calculated by using a linear two-dimensional Doublet-Lattice Method available in MSC&reg / /NASTRAN. These aerodynamic loads were approximated as rational functions of the Laplace variable by using one of the aerodynamic approximation schemes, Roger&amp / #8217 / s approximation, with least-squares method. These approximated aerodynamic loads together with the structural matrices obtained by the finite element method were used to develop the aeroelastic equations of motion of the smart fin in state-space form. The Hinf robust controllers were then designed for the state-space aeroelastic model of the smart fin by considering both SISO (Single-Input Single-Output) and MIMO (Multi-Input Multi-Output) system models. The verification studies of the controllers showed satisfactory flutter suppression performance around the flutter point and a significant improvement in the flutter speed of the smart fin was also observed.

The managing of complex business processes, which are changed due to globalization, calls for the development of powerful information systems that offer generic process modeling and process execution capabilities. Even though contemporary information systems are more and more utilized in enterprises, their actual impact in automatizing complex business process is still limited by the difficulties encountered in design phase. Actually this design phase is time consuming, often subjective and incomplete. In the scope of this study, a reverse approach is followed. Instead of starting with process design, the method of discovering interesting patterns from the navigation traces is taken as basis and a new data analysis methodology named &ldquo / From-to Chart Based Process Discovery&rdquo / is proposed. In this hybrid methodology &ldquo / from-to chart&rdquo / , which is fundamentally dedicated to material handling issues on production floor, is used as the front-end to monitor the transitions among activities of a realistic event log and convert these raw relations into optimum activity sequence. Then a revised version of process mining, which is the back-end of this methodology, upgrades optimum activity sequence into process model.

In mobile computing systems, predicting the next location of a mobile wireless user has gained interest over the past decade. Location prediction may have a wide-range of application areas such as network load balancing, advertising and web page prefetching. In the literature, there exist many location predictors which are divided into two main classes: domain-independent and domain-specific. Song et al. compare the prediction accuracy of the domain-independent predictors from four major families, namely, Markov-based, compression-based, PPM and SPM predictors on Dartmouth&#039 / s campus-wide Wi-Fi mobility data. As a result, the low-order Markov predictors are found as the best predictor. In another work, Bayir et al. propose a domain-specific location predictor (LPMP) as the application of a framework used for discovering mobile cell phone user profiles. In this thesis, we evaluate LPMP and the best Markov predictor with Dartmouth&#039 / s campus-wide Wi-Fi mobility data in terms of accuracy. We also propose a simple method which improves the accuracy of LPMP slightly in the location prediction part of LPMP. Our results show that the accuracy of the best Markov predictor is better than that of LPMP in total. However, interestingly, LPMP yields more accurate results than the best Markov predictor does for the users with the low prediction accuracy.

In this study, meta-analysis of diagnostic tests, Summary Receiver Operating Characteristic (SROC) curve, bivariate random effects and Hierarchical Summary Receiver Operating Characteristic (HSROC) curve theories have been discussed and accuracy in literature of Fine Needle Aspiration (FNA) biopsy that is used in the diagnosis of masses in breast cancer (malignant or benign) has been analyzed. FNA Cytological (FNAC) examination in breast tumor is, easy, effective, effortless, and does not require special training for clinicians. Because of the uncertainty related to FNAC&lsquo / s accurate usage in publications, 25 FNAC studies have been gathered in the meta-analysis. In the plotting of the summary ROC curve, the logit difference and sums of the true positive rates and the false positive rates included in the meta-analysis&lsquo / s codes have been generated by SAS. The formula of the bivariate random effects model and hierarchical summary ROC curve is presented in context with the literature. Then bivariate random effects implementation with the new SAS PROC GLIMMIX is generated. Moreover, HSROC implementation is generated by SAS PROC HSROC NLMIXED. Curves are plotted with RevMan Version 5 (2008). It has been stated that the meta-analytic results of bivariate random effects are nearly identical to the results from the HSROC approach. The results achieved through both random effects meta-analytic methods prove that FNA Cytology is a diagnostic test with a high level of distinguish over breast tumor.

In this thesis, we study two problems. The first one is the renorming problem in Banach lattices. We state the problem and give some known results related to it. Then we pass to construct a positive doubly power bounded operator with a nonpositive inverse on an infinite dimensional AL-space which generalizes the result of [10]. The second problem is related to the mean ergodicity of positive operators on KBspaces. We prove that any positive power bounded operator T in a KB-space E which satisfies lim n!1 dist1 n n&amp / #8722 / 1 Xk=0 Tkx, [&amp / #8722 / g, g] + BE= 0 (8x 2 E, kxk 1), () where BE is the unit ball of E, g 2 E+, and 0 &lt / 1, is mean ergodic and its fixed space Fix(T) is finite dimensional. This generalizes the main result of [12]. Moreover, under the assumption that E is a -Dedekind complete Banach lattice, we prove that if, for any positive power bounded operator T, the condition () implies that T is mean ergodic then E is a KB-space.

Although service oriented architecture has reached a particular maturity level especially in the technological dimension, there is a lack of common and acceptable approach to design a software system through composition and integration of web services. In this thesis, a service oriented system design approach for Service Oriented Architecture based software development is introduced to fill this gap. This new methodology basically offers a procedural top-down decomposition of a given software system allowing several abstraction levels. At the higher levels of the decomposition, the system is divided into abstract nodes that correspond to process models in the decomposition tree. Any node is a process and keeps the sequence and the state information for the possible sub-processes in this decomposition tree. Nodes which are defined as process models may include some sub-nodes to present details for the intermediate levels of the model. Eventually at the leaf level, process models are decomposed into existing web services as the atomic units of system execution. All processes constructing the system decomposition tree are modeled with BPEL (Business Process Execution Language) to expose the algorithmic details of the design. This modeling technique is also supported with a graphical modeling language referred to as SOSEML (Service Oriented Software Engineering Modeling Language) that is also newly introduced in this thesis.

As the tick-by-tick data of financial transactions become easier to reach, processing that much of information in an efficient and correct way to estimate the integrated volatility gains importance. However, empirical findings show that, this much of data may become unusable due to microstructure effects. Most common way to get over this problem is to sample the data in equidistant intervals of calendar, tick or business time scales. The comparative researches on that subject generally assert that, the most successful sampling scheme is a calendar time sampling which samples the data every 5 to 20 minutes. But this generally means throwing out more than 99 percent of the data. So it is obvious that a more efficient sampling method is needed. Although there are some researches on using alternative techniques, none of them is proven to be the best. Our study is concerned with a sampling scheme that uses the information in different scales of frequency and is less prone to microstructure effects. We introduce a new concept of business intensity, the sampler of which is named Optimizable Multiresolution Quadratic Variation Filter. Our filter uses multiresolution analysis techniques to decompose the data into different scales and quadratic variation to build up the new business time scale. Our empirical findings show that our filter is clearly less prone to microstructure effects than any other common sampling method. We use the classified tick-by-tick data for Turkish Interbank FX market. The market is closed for nearly 14 hours of the day, so big jumps occur between closing and opening prices. We also propose a new smoothing algorithm to reduce the effects of those jumps.

Dealing with missing data in spatio-temporal time series constitutes important branch of general missing data problem. Since the statistical properties of time-dependent data characterized by sequentiality of observations then any interruption of consecutiveness in time series will cause severe problems. In order to make reliable analyses in this case missing data must be handled cautiously without disturbing the series statistical properties, mainly as temporal and spatial dependencies. In this study we aimed to compare several imputation methods for the appropriate completion of missing values of the spatio-temporal meteorological time series. For this purpose, several missing imputation methods are assessed on their imputation performances for artificially created missing data in monthly total precipitation and monthly mean temperature series which are obtained from the climate stations of Turkish State Meteorological Service. Artificially created missing data are estimated by using six methods. Single Arithmetic Average (SAA), Normal Ratio (NR) and NR Weighted with Correlations (NRWC) are the three simple methods used in the study. On the other hand, we used two computational intensive methods for missing data imputation which are called Multi Layer Perceptron type Neural Network (MLPNN) and Monte Carlo Markov Chain based on Expectation-Maximization Algorithm (EM-MCMC). In addition to these, we propose a modification in the EM-MCMC method in which results of simple imputation methods are used as auxiliary variables. Beside the using accuracy measure based on squared errors we proposed Correlation Dimension (CD) technique for appropriate evaluation of imputation performances which is also important subject of Nonlinear Dynamic Time Series Analysis.

Current approaches integrating large scale data and information from a variety of sources to reveal molecular basis of cellular events do not adequately benefit from pathway information. Here, we portray a network structure based pathway enrichment system that fuses and exploits model and data: signalling pathways are taken as the biological models while microarray and ChIP-seq data are the sample input data sources among many other alternatives. Our model- and data-driven hybrid system allows to quantitatively assessing the biological activity of a cyclic pathway and simultaneous enrichment of the significant paths leading to the ultimate cellular response. Signal Transduction Score Flow (SiTSFlow) algorithm is the fundamental constituent of proposed network structure based pathway enrichment system. SiTSFlow algorithm converts each pathway into a cascaded graph and then gene scores are mapped onto the protein nodes. Gene scores are transferred to en route of the pathway to form a final activity score describing behaviour of a specific process in the pathway while enriching of the gene node scores. Because of cyclic pathways, the algorithm runs in an iterative manner and it terminates when the node scores converge. The converged final activity score provides a quantitative measure to assess the biological significance of a process under the given experimental conditions. The conversion of cyclic pathways into cascaded graphs is performed by using a linear time multiple source Breadth First Search Algorithm. Furthermore, proposed network structure based pathway enrichment system works in linear time in terms of nodes and edges of given pathways. In order to explore various biological responses of several processes in a global signalling network, the selected small pathways have been unified based on their common gene and process nodes. The merge algorithm for pathways also runs in linear time in terms of nodes and edges of given pathways. In the experiments, SiTSFlow algorithm proved the convergence behaviour of activity scores for several cyclic pathways and for a global signalling network. The biological results obtained by assessing of experimental data by described network structure based pathway enrichment system were in correlation with the expected cellular behaviour under the given experimental conditions.