Semiparametric Estimation of a Gaptime-Associated Hazard Function

This dissertation proposes a suite of novel Bayesian semiparametric estimators for a proportional hazard function associated with the gaptimes, or inter-arrival times, of a counting process in survival analysis. The Cox model is applied and extended in order to identify the subsequent effect of an event on future events in a system with renewal. The estimators may also be applied, without changes, to model the effect of a point treatment on subsequent events, as well as the effect of an event on subsequent events in neighboring subjects. These Bayesian semiparametric estimators are used to analyze the survival and reliability of the New York City electric grid. In particular, the phenomenon of "infant mortality," whereby electrical supply units are prone to immediate recurrence of failure, is flexibly quantified as a period of increased risk. In this setting, the Cox model removes the significant confounding effect of seasonality. Without this correction, infant mortality would be misestimated due to the exogenously increased failure rate during summer months and times of high demand. The structural assumptions of the Bayesian estimators allow the use and interpretation of sparse event data without the rigid constraints of standard parametric models used in reliability studies

Advances in approximate Bayesian computation and trans-dimensional sampling methodology

Bayesian statistical models continue to grow in complexity, driven in part by a few key factors: the massive computational resources now available to statisticians; the substantial gains made in sampling methodology and algorithms such as Markov chain Monte Carlo (MCMC), trans-dimensional MCMC (TDMCMC), sequential Monte Carlo (SMC), adaptive algorithms and stochastic approximation methods and approximate Bayesian computation (ABC); and development of more realistic models for real world phenomena as demonstrated in this thesis for financial models and telecommunications engineering. Sophisticated statistical models are increasingly proposed for practical solutions to real world problems in order to better capture salient features of increasingly more complex data. With sophistication comes a parallel requirement for more advanced and automated statistical computational methodologies. The key focus of this thesis revolves around innovation related to the following three significant Bayesian research questions. 1. How can one develop practically useful Bayesian models and corresponding computationally efficient sampling methodology, when the likelihood model is intractable? 2. How can one develop methodology in order to automate Markov chain Monte Carlo sampling approaches to efficiently explore the support of a posterior distribution, defined across multiple Bayesian statistical models? 3. How can these sophisticated Bayesian modelling frameworks and sampling methodologies be utilized to solve practically relevant and important problems in the research fields of financial risk modeling and telecommunications engineering ? This thesis is split into three bodies of work represented in three parts. Each part contains journal papers with novel statistical model and sampling methodological development. The coherent link between each part involves the novel sampling methodologies developed in Part I and utilized in Part II and Part III. Papers contained in each part make progress at addressing the core research questions posed. Part I of this thesis presents generally applicable key statistical sampling methodologies that will be utilized and extended in the subsequent two parts. In particular it presents novel developments in statistical methodology pertaining to likelihood-free or ABC and TDMCMC methodology. The TDMCMC methodology focuses on several aspects of automation in the between model proposal construction, including approximation of the optimal between model proposal kernel via a conditional path sampling density estimator. Then this methodology is explored for several novel Bayesian model selection applications including cointegrated vector autoregressions (CVAR) models and mixture models in which there is an unknown number of mixture components. The second area relates to development of ABC methodology with particular focus on SMC Samplers methodology in an ABC context via Partial Rejection Control (PRC). In addition to novel algorithmic development, key theoretical properties are also studied for the classes of algorithms developed. Then this methodology is developed for a highly challenging practically significant application relating to multivariate Bayesian $\alpha$-stable models. Then Part II focuses on novel statistical model development in the areas of financial risk and non-life insurance claims reserving. In each of the papers in this part the focus is on two aspects: foremost the development of novel statistical models to improve the modeling of risk and insurance; and then the associated problem of how to fit and sample from such statistical models efficiently. In particular novel statistical models are developed for Operational Risk (OpRisk) under a Loss Distributional Approach (LDA) and for claims reserving in Actuarial non-life insurance modelling. In each case the models developed include an additional level of complexity which adds flexibility to the model in order to better capture salient features observed in real data. The consequence of the additional complexity comes at the cost that standard fitting and sampling methodologies are generally not applicable, as a result one is required to develop and apply the methodology from Part I. Part III focuses on novel statistical model development in the area of statistical signal processing for wireless communications engineering. Statistical models will be developed or extended for two general classes of wireless communications problem: the first relates to detection of transmitted symbols and joint channel estimation in Multiple Input Multiple Output (MIMO) systems coupled with Orthogonal Frequency Division Multiplexing (OFDM); the second relates to co-operative wireless communications relay systems in which the key focus is on detection of transmitted symbols. Both these areas will require advanced sampling methodology developed in Part I to find solutions to these real world engineering problems

Flight Mechanics/Estimation Theory Symposium

Methods of determining satellite orbit and attitude parameters are considered. The Goddard Trajectory Determination System, the Global Positioning System, and the Tracking and Data Relay Satellites are among the satellite navigation systems discussed. Satellite perturbation theory, orbit/attitude determination using landmark data, and star measurements are also covered

Soft-Landing Control of Short-Stroke Reluctance Actuators

Los actuadores de reluctancia se utilizan ampliamente debido a sus altas densidades de fuerza y baja disipación de calor. En particular, los actuadores de reluctancia simples de una sola bobina de carrera corta, como los relés electromecánicos y las electroválvulas, son la mejor opción para operaciones de conmutación de encendido y apagado en muchas aplicaciones debido a su bajo coste, tamaño y masa. Sin embargo, un inconveniente importante es el fuerte impacto al final de cada conmutación, que provoca rebotes, desgaste mecánico y ruido acústico. Son fenómenos muy indeseables que restan valor a las ventajas evidentes de estos actuadores y limitan su rango de aplicaciones potenciales.Esta tesis se centra en el desarrollo y estudio de soluciones de control de aterrizaje suave para actuadores de reluctancia de carrera corta, con el objetivo de minimizar sus velocidades de impacto. Es importante indicar que la eficiencia de dichos dispositivos se produce a costa de serios retos teóricos y prácticos en cuanto a su control, por ejemplo, dinámicas rápidas, híbridas y altamente no lineales, fenómenos electromagnéticos complejos, variabilidad entre unidades y falta de medidas de posición durante el movimiento.El punto de partida es la modelización del sistema, teniendo en cuenta sus subsistemas interconectados eléctricos, magnéticos y mecánicos. El objetivo principal de los modelos es servir para el desarrollo de métodos de control y estimación. Por lo tanto, se trata de modelos de parámetros concentrados expresados como representaciones del espacio de estados. Se especifican diferentes fenómenos electromagnéticos, con especial atención a la histéresis magnética. Se proponen dos tipos de modelos de diferente complejidad según se incorpore o se desprecie el fenómeno de la histéresis magnética.El primer enfoque para el control del aterrizaje suave es el diseño óptimo de las trayectorias de posición y sus correspondientes señales de entrada. La propuesta tiene en cuenta la incertidumbre en la posición del contacto y, por tanto, las soluciones obtenidas son más robustas. Mientras que las señales de entrada generadas son eficaces para las estrategias de control en lazo abierto, las trayectorias de posición generadas pueden utilizarse controles de prealimentación o de retroalimentación.Para mejorar la robustez de los controladores de lazo abierto, también proponemos una estrategia run-to-run que adapta iterativamente las señales de entrada. En concreto, está diseñada para trabajar conjuntamente con un controlador de prealimentación basado en las mencionadas trayectorias de posición construidas de forma óptima. Para el algoritmo de aprendizaje ciclo a ciclo, se elige una técnica de optimización, se ajusta y se compara con dos alternativas.Otro enfoque explorado es el control de retroalimentación para el seguimiento de trayectorias predefinidas de posición. La solución propuesta es un controlador estrictamente conmutativo en modo deslizante. Está enfocado en la simplicidad para facilitar su implementación, al tiempo que se tiene en cuenta la dinámica híbrida. Los análisis teóricos y simulados demuestran que el aterrizaje suave es posible con tasas de muestreo razonables.Los controladores de retroalimentación y otros controladores de seguimiento requieren mediciones o estimaciones precisas de la posición. Como la medición de la posición raramente es práctica, parte de la investigación se dedica al diseño de estimadores de estado. La principal propuesta es un suavizador Rauch-Tung-Striebel ampliado para sistemas no lineales, que incluye varias ideas nuevas relacionadas con el modelo discreto, las entradas y las salidas. Los análisis simulados demuestran que el efecto combinado de las nuevas adiciones da lugar a mucho mejores estimaciones de la posición.Reluctance actuators are widely used due to their high force densities and low heat dissipation. In particular, simple short-stroke single-coil reluctance actuators, such as electromechanical relays and solenoid valves, are the best choice for on-off switching operations in many applications because of their low cost, size and mass. However, a major drawback is the strong impact at the end of each commutation, which provokes bouncing, mechanical wear and acoustic noise. They are very undesirable phenomena that detract from the evident advantages of these actuators and limit their range of potential applications. This thesis focuses on the development and study of soft-landing control solutions for short-stroke reluctance actuators, aiming at minimizing their impact velocities. It is important to indicate that the efficiency of the aforementioned devices comes at the cost of serious theoretical and practical challenges regarding their control, e.g., fast, hybrid and highly nonlinear dynamics, complex electromagnetic phenomena, unit-to-unit variability and lack of position measurements during motion. The starting point is the system modeling, accounting for their interconnected electrical, magnetic and mechanical subsystems. The main purpose of the models is to be used for the development of control and estimation methods. Therefore, they are lumped-parameter models expressed as state-space representations. Different electromagnetic phenomena are specified, with special attention to the magnetic hysteresis. Two model types of different complexities are proposed depending on whether the magnetic hysteresis phenomenon is incorporated or neglected. The first approach for soft-landing control is the optimal design of position trajectories and their corresponding input signals. The proposal considers uncertainty in the contact position, and hence, the obtained solutions are more robust. While the generated input signals are effective for open-loop control strategies, the generated position trajectories can be used in feedforward or feedback control. In order to improve the robustness of open-loop controllers, we also propose a run-to-run strategy that iteratively adapts the input signals. Specifically, it is designed to work in conjunction with a feedforward controller based on the aforementioned optimally constructed position trajectories. For the cycle-to-cycle learning algorithm, an optimization technique is chosen, adjusted and compared to two alternatives. Another explored approach is feedback control for tracking predefined position trajectories. The proposed solution is a purely switching sliding-mode controller. The focus is on simplicity to facilitate its implementation, while also taking into account the hybrid dynamics. Theoretical and simulated analyses show that soft landing is achievable with reasonable sampling rates. Feedback and other tracking controllers require accurate measurements or position estimations. As measuring the position is rarely practical, part of the research is devoted to the design of state estimators. The main proposal is an extended Rauch–Tung–Striebel smoother, which includes several new ideas regarding the discrete model, the inputs and the outputs. Simulated analyses demonstrate that the combined effect of the novel additions results in much better position estimations.<br /

Interpretable Machine Learning for Electro-encephalography

While behavioral, genetic and psychological markers can provide important information about brain health, research in that area over the last decades has much focused on imaging devices such as magnetic resonance tomography (MRI) to provide non-invasive information about cognitive processes. Unfortunately, MRI based approaches, able to capture the slow changes in blood oxygenation levels, cannot capture electrical brain activity which plays out on a time scale up to three orders of magnitude faster. Electroencephalography (EEG), which has been available in clinical settings for over 60 years, is able to measure brain activity based on rapidly changing electrical potentials measured non-invasively on the scalp. Compared to MRI based research into neurodegeneration, EEG based research has, over the last decade, received much less interest from the machine learning community. But generally, EEG in combination with sophisticated machine learning offers great potential such that neglecting this source of information, compared to MRI or genetics, is not warranted. In collaborating with clinical experts, the ability to link any results provided by machine learning to the existing body of research is especially important as it ultimately provides an intuitive or interpretable understanding. Here, interpretable means the possibility for medical experts to translate the insights provided by a statistical model into a working hypothesis relating to brain function. To this end, we propose in our first contribution a method allowing for ultra-sparse regression which is applied on EEG data in order to identify a small subset of important diagnostic markers highlighting the main differences between healthy brains and brains affected by Parkinson's disease. Our second contribution builds on the idea that in Parkinson's disease impaired functioning of the thalamus causes changes in the complexity of the EEG waveforms. The thalamus is a small region in the center of the brain affected early in the course of the disease. Furthermore, it is believed that the thalamus functions as a pacemaker - akin to a conductor of an orchestra - such that changes in complexity are expressed and quantifiable based on EEG. We use these changes in complexity to show their association with future cognitive decline. In our third contribution we propose an extension of archetypal analysis embedded into a deep neural network. This generative version of archetypal analysis allows to learn an appropriate representation where every sample of a data set can be decomposed into a weighted sum of extreme representatives, the so-called archetypes. This opens up an interesting possibility of interpreting a data set relative to its most extreme representatives. In contrast, clustering algorithms describe a data set relative to its most average representatives. For Parkinson's disease, we show based on deep archetypal analysis, that healthy brains produce archetypes which are different from those produced by brains affected by neurodegeneration

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications