Topic of mergers and acquisitions (M&A) is popular both in academia and financial circles and press. A great deal of research has been focused on the value creation side of M&A deals, nonetheless factors influencing the particular method of payment used in M&A transactions are equally interesting. This thesis focuses on number of factors influencing the choice of medium of exchange in M&A deals with European Union domiciled bidders. Using Bayesian model averaging and a relatively new dataset of transactions announced between 2010 and 2018, the analysis finds several bidder, target and deal specific characteristics to be of a provable effect on the choice of payment. Finally, several enhancements and research questions for a further research are identified.

This manuscript consists of two papers that formulate novel methodologies pertaining to time series analysis in the state-space framework. In Chapter 1, we introduce an innovative time series forecasting procedure that relies on model-based clustering and model averaging. The clustering algorithm employs a state-space model comprised of three latent structures: a long-term trend component; a seasonal component, to capture recurring global patterns; and an anomaly component, to reflect local perturbations. A two-step clustering algorithm is applied to identify series that are both globally and locally correlated, based on the corresponding smoothed latent structures. For each series in a particular cluster, a set of forecasting models is fit, using covariate series from the same cluster. To fully utilize the cluster information and to improve forecasting for a series of interest, multi-model averaging is employed. We illustrate the proposed technique in an application that involves a collection of monthly disease incidence series. In Chapter 2, to effectively characterize a count time series that arises from a zero-inflated binomial (ZIB) distribution, we propose two classes of statistical models: a class of observation-driven ZIB (ODZIB) models, and a class of parameter-driven ZIB (PDZIB) models. The ODZIB model is formulated in the partial likelihood framework. Common iterative algorithms (Newton-Raphson, Fisher Scoring, and Expectation Maximization) can be used to obtain the maximum partial likelihood estimators (MPLEs). The PDZIB model is formulated in the state-space framework. For parameter estimation, we devise a Monte Carlo Expectation Maximization (MCEM) algorithm, using particle methods to approximate the intractable conditional expectations in the E-step of the algorithm. We investigate the efficacy of the proposed methodology in a simulation study, and illustrate its utility in a practical application pertaining to disease coding.

Prohibitive cost and large size of conventional angular rate sensors have limited their use to large scale aeronautical applications. However, the emergence of MEMS technology in the last two decades has enabled angular rate sensors to be fabricated that are orders of magnitude smaller in size and in cost. The reduction in size and cost has subsequently encouraged new applications to emerge, but the accuracy and resolution of MEMS angular rate sensors will have to be greatly improved before they can be successfully utilised for such high end applications as inertial navigation. MEMS angular rate sensors consist of a vibratory structure with two main resonant modes and high Q factors. By means of an external excitation, the device is driven into a constant amplitude sinusoidal vibration in the first mode, normally at resonance. When the device is subject to an angular rate input, Coriolis acceleration causes a transfer of energy between the two modes and results in a sinusoidal motion in the second mode, whose amplitude is a measure of the input angular rate. Ideally the only coupling between the two modes is the Coriolis acceleration, however fabrication imperfections always result in some cross stiffness and cross damping effects between the two modes. Much of the previous research work has focussed on improving the physical structure through advanced fabrication techniques and structural design; however attention has been directed in recent years to the use of control strategies to compensate for these structural imperfections. The performance of the MEMS angular rate sensors is also hindered by the effects of time varying parameter values as well as noise sources such as thermal-mechanical noise and sensing circuitry noise. In this thesis, MEMS angular rate sensing literature is first reviewed to show the evolu- tion of MEMS angular rate sensing from the basic principles of open-loop operation to the use of complex control strategies designed to compensate for any fabrication imperfections and time-varying effects. Building on existing knowledge, a novel adaptively controlled MEMS triaxial angular rate sensor that uses a single vibrating mass is then presented. Ability to sense all three components of the angular rate vector with a single vibrating mass has advantages such as less energy usage, smaller wafer footprint, avoidance of any mechanical interference between multiple resonating masses and removal of the need for precise alignment of three separate devices. The adaptive controller makes real-time estimates of the triaxial angular rates as well as the device cross stiffness and cross damping terms. These estimates are then used to com- pensate for their effects on the vibrating mass, resulting in the mass being controlled to follow a predefined reference model trajectory. The estimates are updated using the error between the reference model trajectory and the mass's real trajectory. The reference model trajectory is designed to provide excitation to the system that is sufficiently rich to enable all parameter estimates to converge to their true values. Avenues for controller simplification and optimisation are investigated through system simulations. The triaxial controller is analysed for stability, averaged convergence rate and resolution. The convergence rate analysis is further utilised to determine the ideal adaptation gains for the system that minimises the unwanted oscillatory behaviour of the parameter estimates. A physical structure for the triaxial device along with the sensing and actuation means is synthesised. The device is realisable using MEMS fabrication techniques due to its planar nature and the use of conventional MEMS sensing and actuation elements. Independent actuation and sensing is achieved using a novel checkerboard electrode arrangement. The physical structure is refined using a design automation process which utilises finite element analysis (FEA) and design optimisation tools that adjust the design variables until suitable design requirements are met. Finally, processing steps are outlined for the fabrication of the device using a modified, commercially available polysilicon MEMS process.

This thesis concerns the phylogenetic relationships and the evolution of the gall-inducing wasps belonging to the family Cynipidae. Several previous studies have used morphological data to reconstruct the evolution of the family. DNA sequences from several mitochondrial and nuclear genes where obtained and the first molecular, and combined molecular and morphological, analyses of higher-level relationships in the Cynipidae is presented. A Bayesian approach to data analysis is adopted, and models allowing combined analysis of heterogeneous data, such as multiple DNA data sets and morphology, are developed. The performance of these models is evaluated using methods that allow the estimation of posterior model probabilities, thus allowing selection of most probable models for the use in phylogenetics. The use of Bayesian model averaging in phylogenetics, as opposed to model selection, is also discussed. It is shown that Bayesian MCMC analysis deals efficiently with complex models and that morphology can influence combined-data analyses, despite being outnumbered by DNA data. This emphasizes the utility and potential importance of using morphological data in statistical analyses of phylogeny. The DNA-based and combined-data analyses of cynipid relationships differ from previous studies in two important respects. First, it was previously believed that there was a monophyletic clade of woody rosid gallers but the new results place the non-oak gallers in this assemblage (tribes Pediaspidini, Diplolepidini, and Eschatocerini) outside the rest of the Cynipidae. Second, earlier studies have lent strong support to the monophyly of the inquilines (tribe Synergini), gall wasps that develop inside the galls of other species. The new analyses suggest that the inquilines either originated several times independently, or that some inquilines secondarily regained the ability to induce galls. Possible reasons for the incongruence between morphological and DNA data is discussed in terms of heterogeneity in evolutionary rates among lineages, and convergent evolution of morphological characters.

In this paper we use the long-term empirical relationship among primary energy consumption, real income, physical capital, population and technology, obtained by averaged panel error correction models, to project the long-term primary energy consumption of 56 countries up to 2100. In forecasting long-term primary energy consumption, we work with four different Shared Socioeconomic Pathway Scenarios (SSPs) developed for the Intergovernmental Panel on Climate Change (IPCC) framework, assuming different challenges to adaptation and mitigation. We find that in all scenarios, China, the United States and India will be the largest energy consumers, while highly growing countries will also significantly contribute to energy use. We observe for most scenarios a sharp increase in global energy consumption, followed by a levelling-out and a decrease towards the second half of the century. The reasons behind this pattern are not only slower population growth, but also infrastructure saturation and increased total factor productivity. This means, as countries move towards more knowledge based societies, and higher energy efficiency, their primary energy usage is likely to decrease as a result. Global primary energy consumption is expected however to increase significantly in the coming decades, thus increasing the pressure on policy makers to cope with the questions of energy security and greenhouse gas mitigation at the same time. (authors' abstract) / Series: Department of Economics Working Paper Series

Supercritical fluid extraction has been used to recover biomaterials from natural matrices. Mathematical modeling of the extraction is required for process design and scale up. Existing models in literature are correlative and dependent upon the experimental data. Construction of predictive models giving reliable results in the lack of experimental data is precious. The long term objective of this study was to construct a predictive mass transfer model, representing supercritical fluid extraction of biomaterials in packed beds by the method of volume averaging. In order to develop mass transfer equations in terms of volume averaged variables, velocity and velocity deviation fields, closure variables were solved for a specific case and the coefficients of volume averaged mass transfer equation for the specific case were computed using one and two-dimensional geometries via analytical and numerical solutions, respectively. Spectral Element method with Domain Decomposition technique, Preconditioned Conjugate Gradient algorithm and Uzawa method were used for the numerical solution. The coefficients of convective term with additional terms of volume averaged mass transfer equation were similar to superficial velocity. The coefficients of dispersion term were close to diffusivity of oil in supercritical carbon dioxide. The coefficients of interphase mass transfer term were overestimated in both geometries. Modifications in boundary conditions, change in geometry of particles and use of three-dimensional computations would improve the value of the coefficient of interphase mass transfer term.

Growth release of trees following canopy disturbances is of interest to ecological scientists and forest managers. Using dendroecological techniques, I examined growth release of canopy and subcanopy trees following the formation of natural, fine-scale canopy gaps in old-growth, western red cedar-western hemlock forests of coastal British Columbia. I aimed to quantify detailed information on release of the three shade-tolerant tree species that constitute these stands: western red cedar (Thuja plicata), western hemlock (Tsuga heterophylla), and Pacific silver fir (Abies amabilis). As a first step, I calibrated the radial-growth averaging method to account for regional-scale variability and capture a more complete range of growth releases that may occur following the formation of fine-scale gaps in the study stands. A 25% threshold, 5-year moving average, and 10-year window emerged as appropriate parameters for detecting releases using radial-growth averaging. Basal area increment was also the most appropriate growth index for detecting releases. Establishing these empirically-based criteria was important for quantifying the magnitude and duration of releases. Tree diameter and growth rate prior to release were the most important predictors of the magnitude and duration of releases, but identity of the tree species and distance from the gap center were also important predictors. Western hemlock and Pacific silver fir were often growing slowly both in the canopy and subcanopy, giving them tremendous potential to release. For these species, releases were generally intensive and persistent. In contrast, western red cedar were often growing quickly both in the canopy and subcanopy, giving them less potential to release. Compared to western hemlock and Pacific silver fir, western red cedar releases were less intensive and persistent. Patterns related to distance from the gap center emerged for trees growing along the north-south axis of gaps. Regardless of species, increasing distance from the gap center resulted in decreasing magnitude and duration of releases. However, patterns for duration were complex, as the distance effect was greater for trees north of the gap center. Information on growth release of trees is useful for reconstructing the history of past canopy disturbances, elucidating mechanisms of tree species coexistence, and assessing and predicting stand changes due to forest management in coastal British Columbia.

Model-based clustering is based on a finite mixture of distributions, where each mixture component corresponds to a different group, cluster, subpopulation, or part thereof. Gaussian mixture distributions are most often used. Criteria commonly used in choosing the number of components in a finite mixture model include the Akaike information criterion, Bayesian information criterion, and the integrated completed likelihood. The best model is taken to be the one with highest (or lowest) value of a given criterion. This approach is not reasonable because it is practically impossible to decide what to do when the difference between the best values of two models under such a criterion is ‘small’. Furthermore, it is not clear how such values should be calibrated in different situations with respect to sample size and random variables in the model, nor does it take into account the magnitude of the likelihood. It is, therefore, worthwhile considering a model-averaging approach. We consider an averaging of the top M mixture models and consider applications in clustering and classification. In the course of model averaging, the top M models often have different numbers of mixture components. Therefore, we propose a method of merging Gaussian mixture components in order to get the same number of clusters for the top M models. The idea is to list all the combinations of components for merging, and then choose the combination corresponding to the biggest adjusted Rand index (ARI) with the ‘reference model’. A weight is defined to quantify the importance of each model. The effectiveness of mixture model averaging for clustering is proved by simulated data and real data under the pgmm package, where the ARI from mixture model averaging for clustering are greater than the one of corresponding best model. The attractive feature of mixture model averaging is it’s computationally efficiency; it only uses the conditional membership probabilities. Herein, Gaussian mixture models are used but the approach could be applied effectively without modification to other mixture models. / Paul McNicholas

<p>This thesis discusses novel developments in Bayesian analytics for high-dimensional multivariate time series. The focus is on the class of multiregression dynamic models (MDMs), which can be decomposed into sets of univariate models processed in parallel yet coupled for forecasting and decision making. Parallel processing greatly speeds up the computations and vastly expands the range of time series to which the analysis can be applied. </p><p>I begin by defining a new sparse representation of the dependence between the components of a multivariate time series. Using this representation, innovations involve sparse dynamic dependence networks, idiosyncrasies in time-varying auto-regressive lag structures, and flexibility of discounting methods for stochastic volatilities.</p><p>For exploration of the model space, I define a variant of the Shotgun Stochastic Search (SSS) algorithm. Under the parallelizable framework, this new SSS algorithm allows the stochastic search to move in each dimension simultaneously at each iteration, and thus it moves much faster to high probability regions of model space than does traditional SSS. </p><p>For the assessment of model uncertainty in MDMs, I propose an innovative method that converts model uncertainties from the multivariate context to the univariate context using Bayesian Model Averaging and power discounting techniques. I show that this approach can succeed in effectively capturing time-varying model uncertainties on various model parameters, while also identifying practically superior predictive and lucrative models in financial studies. </p><p>Finally I introduce common state coupled DLMs/MDMs (CSCDLMs/CSCMDMs), a new class of models for multivariate time series. These models are related to the established class of dynamic linear models, but include both common and series-specific state vectors and incorporate multivariate stochastic volatility. Bayesian analytics are developed including sequential updating, using a novel forward-filtering-backward-sampling scheme. Online and analytic learning of observation variances is achieved by an approximation method using variance discounting. This method results in faster computation for sequential step-ahead forecasting than MCMC, satisfying the requirement of speed for real-world applications. </p><p>A motivating example is the problem of short-term prediction of electricity demand in a "Smart Grid" scenario. Previous models do not enable either time-varying, correlated structure or online learning of the covariance structure of the state and observational evolution noise vectors. I address these issues by using a CSCMDM and applying a variance discounting method for learning correlation structure. Experimental results on a real data set, including comparisons with previous models, validate the effectiveness of the new framework.</p> / Dissertation

Current and future legislation demands ever decreasing levels of pollution from gas turbine engines, and with combustor performance playing a critical role in resultant emissions, a need exists to develop a greater appreciation of the fundamental causes of unsteadiness. Particle Image Velocimetry (PIV) provides a platform to enable such investigations. This thesis presents the development of PIV measurement methodologies for highly turbulent flows. An appraisal of these techniques applied to gas turbine combustors is then given, finally allowing a description of the increased understanding of the underlying fluid dynamic processes within combustors to be provided. Through the development of best practice optimisation procedures and correction techniques for the effects of sub-grid filtering, high quality PN data has been obtained. Time average statistical data at high spatial resolution has been collected and presented for generic and actual combustor geometry providing detailed validation of the turbulence correction methods developed, validation data for computational studies, and increased understanding of flow mechanisms. These data include information not previously available such as turbulent length scales. Methodologies developed for the analysis of instantaneous PIV data have also allowed the identification of transient flow structures not seen previously because they are invisible in the time average. Application of a new `PDF conditioning' technique has aided the explanation of calculated correlation functions: for example, bimodal primary zone recirculation behaviour and jet misalignments were explained using these techniques. Decomposition of the velocity fields has also identified structures present such as jet shear layer vortices, and through-port swirling motion. All of these phenomena are potentially degrading to combustor performance and may result in flame instability, incomplete combustion, increased noise and increased emissions.