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Maternal Smoking Impact on the Delivery Cost: a population-based study in the Emilia-Romagna regionBalinskaite, Violeta <1980> 21 January 2014 (has links)
This doctoral thesis is devoted to the study of the causal effects of the maternal smoking on the delivery cost. The interest of economic consequences of smoking in pregnancy have been studied fairly extensively in the USA, and very little is known in European context.
To identify the causal relation between different maternal smoking status and the delivery cost in the Emilia-Romagna region two distinct methods were used. The first - geometric multidimensional - is mainly based on the multivariate approach and involves computing and testing the global imbalance, classifying cases in order to generate well-matched comparison groups, and then computing treatment effects. The second - structural modelling - refers to a general methodological account of model-building and
model-testing. The main idea of this approach is to decompose the global mechanism into sub-mechanisms though a recursive decomposition of a multivariate distribution.
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Optimal choices: mean field games with controlled jumps and optimality in a stochastic volatility modelBenazzoli, Chiara January 2018 (has links)
Decision making in continuous time under random influences is the leitmotif of this work.
In the first part a family of mean field games with a state variable evolving as a jump-diffusion process is studied. Under fairly general conditions, the existence of a solution in a relaxed version of these games is established and conditions under which the optimal strategies are in fact Markovian are given. The proofs rely upon the notions of relaxed controls and martingale problems. Mean field games represent the limit, as the number of players tends to infinity, of nonzero-sum stochastic differential games. Under the assumption that the former admit a regular Markovian solution, an approximate Nash equilibrium for the corresponding n-player games is constructed, and the rate of convergence is provided. Finally, the general theory is applied to a simple illiquid inter-bank market model, where the banks can adjust their reserves only at the jump times of some given Poisson processes with a common constant intensity, and some numerical results are provided.
In the second part a stochastic optimization problem is presented. Here the evolution of the state is modeled as in the Heston model, but with a further multiplicative control input in the volatility term. The main objective is to consider the possible role of an external actor, whose exogenous contribution is summarised in the control itself. The solvability of the Hamilton-Jacobi-Bellman equation associated to this optimal control problem is discussed. Read more
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Delayed Forward-Backward stochastic PDE’s driven by non Gaussian Lévy noise with application in financeCordoni, Francesco Giuseppe January 2016 (has links)
From the very first results, the mathematical theory of financial markets has undergone several changes, mostly due to financial crises who forced the mathematical-economical community to change the basic assumptions on which the whole theory is founded. Consequently a new mathematical foundation were needed. In particular, the 2007/2008 credit crunch showed the word that a new financial theoretical framework was necessary, since several empirical evidences emerged that aspects that were neglected prior to these years were in fact fundamental if one has to deal with financial markets. The goal of the present thesis goes in this direction; we aim at developing rigorous mathematical instruments that allow to treat fundamental problems in modern financial mathematics. In order to do so, the talk is thus divided into three main parts, which focus on three different topics of modern financial mathematics. The first part is concerned with delay equations. In particular, we will prove Feynman-Kac type result for BSDE's with time-delayed generator, as well as an ad hoc Ito formula for delay equations with jumps. The second part deal with infinite dimensional analysis and network models, focusing in particular on existence and uniqueness results for infinite dimensional SPDE's on networks with general non-local boundary conditions. The last part treats the topic of rigorous asymptotic expansions, providing a small noise asymptotic expansion for SDE with Lévy noise with several concrete application to financial models. Read more
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Heavy-tailed Phenomena and Tail Index InferenceJia, Mofei January 2014 (has links)
This thesis focuses on the analysis of heavy-tailed distributions, which are widely applied to model phenomena in many disciplines. The definition of heavy tails based on the theory of regular variation highlights the importance of the tail index, which indicates the existence of moments and characterises the rate at which the tail decays. Two new approaches to make inference for the tail index are proposed. The first approach employs a regression technique and constructs an estimator of the tail index. It exploits the fact that the behaviour of the characteristic function near the origin reflects the behaviour of the distribution function at infinity. The main advantage of this approach is that it utilises all observations to constitute each point in the regression, not just extreme values. Moreover, the approach does not rely on prior information on the starting point of the tail behaviour of the underlying distribution and shows excellent performance in a wide range of cases: Pareto distributions, heavy-tailed distributions with a non-constant slowly varying factor, and composite distributions with heavy tails. The second approach is motivated by the asymptotic properties of a special moment statistic, the so-called partition function. This statistic considers blocks of data and is generally used in the context of multifractality. Due to the interplay between the weak law of large numbers and the generalised central limit theorem, the asymptotic behaviour of the partition function is strongly affected by the existence of moments even for weakly dependent samples. Via a quantity, the scaling function, a graphical method to identify the existence of heavy tails is proposed. Moreover, the plot of the scaling function allows one to make inference for the underlying distribution: with infinite variance, finite variance with tail index larger than two, or all moments finite. Furthermore, since the tail index is reflected at the breakpoint of the plot of the scaling function, this gives the possibility to estimate the tail index. Both these two approaches use the entire distribution, not just the tail, to analyse the tail behaviour. This sheds a new light on the analysis of heavy-tailed distributions. At the end of this thesis, these two approaches are used to detect power laws in empirical data sets from a variety of fields and contribute to the debate on whether city sizes are better approximated by a power law or a log-normal distribution. Read more
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Invariants estimation in nonlinear time seriesSardonini, Laura <1979> 26 March 2007 (has links)
No description available.
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Clustering of variables around latent components: an application in consumer scienceEndrizzi, Isabella <1975> 02 April 2008 (has links)
The present work proposes a method based on CLV (Clustering around Latent
Variables) for identifying groups of consumers in L-shape data. This kind of datastructure
is very common in consumer studies where a panel of consumers is asked to
assess the global liking of a certain number of products and then, preference scores are
arranged in a two-way table Y. External information on both products (physicalchemical
description or sensory attributes) and consumers (socio-demographic
background, purchase behaviours or consumption habits) may be available in a row
descriptor matrix X and in a column descriptor matrix Z respectively. The aim of this
method is to automatically provide a consumer segmentation where all the three
matrices play an active role in the classification, getting homogeneous groups from all
points of view: preference, products and consumer characteristics.
The proposed clustering method is illustrated on data from preference studies on food
products: juices based on berry fruits and traditional cheeses from Trentino. The
hedonic ratings given by the consumer panel on the products under study were
explained with respect to the product chemical compounds, sensory evaluation and
consumer socio-demographic information, purchase behaviour and consumption habits. Read more
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Genome characterization through a mathematical model of the genetic code: an analysis of the whole chromosome 1 of A. thalianaProperzi, Enrico <1976> 18 February 2013 (has links)
The objective of this work is to characterize the genome of the chromosome 1 of A.thaliana, a small flowering plants used as a model organism in studies of biology and genetics, on the basis of a recent mathematical model of the genetic code.
I analyze and compare different portions of the genome: genes, exons, coding sequences (CDS), introns, long introns, intergenes, untranslated regions (UTR) and regulatory sequences. In order to accomplish the task, I transformed nucleotide sequences into binary sequences based on the definition of the three different dichotomic classes.
The descriptive analysis of binary strings indicate the presence of regularities in each portion of the genome considered. In particular, there are remarkable differences between coding sequences (CDS and exons) and non-coding sequences, suggesting that the frame is important only for coding sequences and that dichotomic classes can be useful to recognize them.
Then, I assessed the existence of short-range dependence between binary sequences computed on the basis of the different dichotomic classes.
I used three different measures of dependence: the well-known chi-squared test and two indices derived from the concept of entropy i.e. Mutual Information (MI) and Sρ, a normalized version of the “Bhattacharya Hellinger Matusita distance”.
The results show that there is a significant short-range dependence structure only for the coding sequences whose existence is a clue of an underlying error detection and correction mechanism.
No doubt, further studies are needed in order to assess how the information carried by dichotomic classes could discriminate between coding and noncoding sequence and, therefore, contribute to unveil the role of the mathematical structure in error detection and correction mechanisms. Still, I have shown the potential of the approach presented for understanding the management of genetic information. Read more
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Brain Decoding for Brain Mapping: Definition, Heuristic Quantification, and Improvement of Interpretability in Group MEG DecodingKia, Seyed Mostafa January 2017 (has links)
In the last century, a huge multi-disciplinary scientific endeavor is devoted to answer the historical questions in understanding the brain functions. Among the statistical methods used for this purpose, brain decoding provides a tool to predict the mental state of a human subject based on the recorded brain signal. Brain decoding is widely applied in the contexts of brain-computer interfacing, medical diagnosis, and multivariate hypothesis testing on neuroimaging data. In the latest case, linear classifiers are generally employed to discriminate between experimental conditions. Then, the derived weights are visualized in the form of brain maps to further study the spatio-temporal patterns of the underlying neurophysiological activity. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal-to-noise ratio, across-subject variability, and the high dimensionality of the neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this thesis, as the primary contribution, we propose a theoretical definition of interpretability in linear brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. As an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. We propose to combine the approximated interpretability and the generalization performance of the model into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future. As the secondary contribution, we present an application of multi-task joint feature learning for group-level multivariate pattern recovery in single-trial MEG decoding. The proposed method allows for recovering sparse yet consistent patterns across different subjects, and therefore enhances the interpretability of the decoding model. We evaluated the performance of the multi-task joint feature learning in terms of generalization, reproducibility, and quality of pattern recovery against traditional single-subject and pooling approaches on both simulated and real MEG datasets. Our experimental results demonstrate that the multi-task joint feature learning framework is capable of recovering meaningful patterns of varying spatio-temporally distributed brain activity across individuals while still maintaining excellent generalization performance. The presented methodology facilitates the application of brain decoding for characterizing the fine-level distinctive patterns of brain activity in group-level inference on neuroimaging data. Read more
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Predictive networks for multi meta-omics data integrationZandonà, Alessandro January 2017 (has links)
The role of microbiome in disease onset and in equilibrium is being exposed by a wealth of high-throughput omics methods. All key research directions, e.g., the study of gut microbiome dysbiosis in IBD/IBS, indicate the need for bioinformatics methods that can model the complexity of the microbial communities ecology and unravel its disease-associated perturbations. A most promising direction is the “meta-omics” approach, that allows a profiling based on various biological molecules at the metagenomic scale (e.g., metaproteomics, metametabolomics) as well as different “microbial” omes (eukaryotes and viruses) within a system biology approach. This thesis introduces a bioinformatic framework for microbiota datasets that combines predictive profiling, differential network analysis and meta-omics integration. In detail, the framework identifies biomarkers discriminating amongst clinical phenotypes, through machine learning techniques (Random Forest or SVM) based on a complete Data Analysis Protocol derived by two initiatives funded by FDA: the MicroArray Quality Control-II and Sequencing Quality Control projects. The biomarkers are interpreted in terms of biological networks: the framework provides a setup for networks inference, quantification of networks differences based on the glocal Hamming and Ipsen-Mikhailov (HIM) distance and detection of network communities. The differential analysis of networks allows the study of microbiota structural organization as well as the evolving trajectories of microbial communities associated to the dynamics of the target phenotypes. Moreover, the framework combines a novel similarity network fusion method and machine learning to identify biomarkers from the integration of multiple meta-omics data. The framework implementation requires only standard open source computational biology tools, as a combination of R/Bioconductor and Python functions. In particular, full scripts for meta-omics integration are available in a GitHub repository to ease reuse (https://github.com/AleZandona/INF). The pipeline has been validated on original data from three different clinical datasets. First, the predictive profiling and the network differential analysis have been applied on a pediatric Inflammatory Bowel Disease (IBD) cohort (in faecal vs biopsy environments) and controls, in collaboration with a multidisciplinary team at the Ospedale Pediatrico Bambino Gesú (Rome, I). Then, the meta-omics integration has been tested on a paired bacterial and fungal gut microbiota human IBD datasets from the Gastroenterology Department of the Saint Antoine Hospital (Paris, F), thanks to the collaboration with “Commensals and Probiotics-Host Interactions” team at INRA (Jouy-en-Josas, F). Finally, the framework has been validated on a bacterial-fungal gut microbiota dataset from children affected by Rett syndrome. The different nature of datasets used for validation naturally supports the extension of the framework on different omics datasets. Besides, clinical practice can take advantage of our framework, given the reproducibility and robustness of results, ensured by the adopted Data Analysis Protocol, as well as the biological relevance of the findings, confirmed by the clinical collaborators. Specifically, the omics-based dysbiosis profiles and the inferred biological networks can support the current diagnostic tools to reveal disease-associated perturbations at a much prodromal earlier stage of disease and may be used for disease prevention, diagnosis and prognosis. Read more
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Quantifying Urban Social Well-Being using Mobile Phone DataGundogdu, Didem January 2018 (has links)
Today, more than half of the world population is living in cities, which has been doubled in the last 50 years. The reason for that attraction is not only economical, but also security, education, and health. While people migrate to cities to reach improved life conditions, several issues raised by the increasing population. Recent studies have shown the importance of ethnic and cultural diversity of urban population to encourage tolerance, and to foster creativity and economic growth. Facing the urban growth challenges, we search for the key formulas to obtain healthy societies under the light of new type of data sources, such as mobile phone usage datasets. To this end, first we build up a tool to identify security related incidents from a country, which unstable political conditions held. Then we trace the formulas of healthy societies with examples from both developing and developed countries. We check the individual interaction and communication pattern effects (bridging and bonding) for the existence of social capital. Then we analyze aggregated ethnic diversity, and associate segregation scores with census data, and different ethnic groups preferences to move in the city, existence of any pattern for specific nation. The current studies are mainly hypothetical, with the absence of large scale real life data sources. This thesis aims to provide an insight to policy makers for building healthy societies, for the benefit of urban well-being. Read more
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