Bayesian Nonparametric Modelling of the Return Distribution with Stochastic Volatility

This paper presents a method for Bayesian nonparametric analysis of the return distribution in a stochastic volatility model. The distribution of the logarithm of the squared return is flexibly modelled using an infinite mixture of Normal distributions. This allows efficient Markov chain Monte Carlo methods to be developed. Links between the return distribution and the distribution of the logarithm of the squared returns are discussed. The method is applied to simulated data, one asset return series and one stock index return series. We find that estimates of volatility using the model can differ dramatically from those using a Normal return distribution if there is evidence of a heavy-tailed return distribution

Bayesian nonparametric modelling of financial data

This thesis presents a class of discrete time univariate stochastic volatility models using Bayesian nonparametric techniques. In particular, the models that will be introduced are not only the basic stochastic volatility model, but also the heavy-tailed model using scale mixture of Normals and the leverage model. The aim will be focused on capturing flexibly the distribution of the logarithm of the squared return under the aforementioned models using infinite mixture of Normals. Parameter estimates for these models will be obtained using Markov chain Monte Carlo methods and the Kalman filter. Links between the return distribution and the distribution of the logarithm of the squared returns "fill be established. The one-step ahead predictive ability of the model will be measured using log-predictive scores. Asset returns, stock indices and exchange rates will be fitted using the developed methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Bayesian algorithm for detecting differentially expressed proteins and its application in breast cancer research

Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.European Commission - Seventh Framework Programme (FP7)Science Foundation IrelandThe Irish Cancer Societ

Classification of periodontal diseases: history, present and future

Classification of periodontitis patients has been a challenge troughout the last 100 years. Historically, the classification of peridontal disease has been directly linked to the available knowledge about the clinical symptoms and the pathogenesis of the disease. Thus, throughout the years and paralel tot he development of knowledge, disease classification has evolved and has been updated several times. Currently, the internationally accepted classification system is the one proposed by Armitage (1999). However, also other disease clasification systems have been proposed aiming for an easier clinical applicability (Van der Velden 2000). Despit the efforts, classification remains an issue of controversy and all the different proposed systems present advantages and shortcomings. New knowledge of the disease etiology and pathobiology, as well as contemporary bioinformatics techniques, could contribute to the improvement of the current classification systems

A Bayesian algorithm for detecting differentially expressed proteins and its application in breast cancer research

Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.European Commission - Seventh Framework Programme (FP7)Science Foundation IrelandThe Irish Cancer Societ