Objective. Major depressive disorder (MDD) is a common condition following treatment in the Intensive Care Unit (ICU). Long-term data on MDD in chronically critically ill (CCI) patients are scarce. Hence, the primary aim of the present study was to investigate the frequency and predictors of MDD after intensive care of CCI patients. Materials and Methods. In a prospective cohort study, patients with long-term mechanical ventilation requirements () were assessed with respect to a diagnosis of MDD, using the Structured Clinical Interview for DSM-IV, three and six months after the transfer from acute ICU to post-acute ICU. Sociodemographic, psychological, and clinical risk factors with values ≤ 0.1 were identified in a univariate logistic regression analysis and entered in a multivariable logistic regression model. A mediator analysis was run using the bootstrapping method, testing the mediating effect of perceived helplessness during the ICU stay, between the recalled traumatic experience from the ICU and a post-ICU MDD. Results. 17.6% () of the patients showed a full- or subsyndromal MDD. Perceived helplessness, recalled experiences of a traumatic event from the ICU, symptoms of acute stress disorder, and the diagnosis of posttraumatic stress disorder (PTSD) after ICU could be identified as significant predictors of MDD. In a mediator analysis, perceived helplessness could be proved as a mediator. Conclusions. Every fifth CCI patient suffers from MDD up to six months after being discharged from ICU. Particularly, perceived helplessness during the ICU stay seems to mainly affect the long-term evolvement of MDD. CCI patients with symptoms of acute stress disorder/PTSD should also be screened for MDD.

The purpose of this study was to understand what perceptions the social workers in the social services perceives the clients have about the social services and how this can affect the social workers in their work and private life. The data in this study was collected through a survey, conducted by social workers in six different municipalities. The study has been carried out through a mixed method research. The data was thereafter analyzed through a univariate analysis and a content analysis. The findings in this study shows that a majority of the social workers feel that the clients have bad perceptions about the social services. These are based on several factors, such as own experiences and feelings of shame. Media and rumors also play a significant role. Furthermore, the study also shows that the social workers are most affected in their work than in their private life. Another finding the study showed is that support from the director and the colleagues is a way for social workers to manage the clients' perceptions about them. Explaining and clarifying to the client and the public what the social service means, is an another way of managing it.

Les données manquantes constituent un challenge commun en reconnaissance de forme et traitement de signal. Une grande partie des techniques actuelles de ces domaines ne gère pas l'absence de données et devient inutilisable face à des jeux incomplets. L'absence de données conduit aussi à une perte d'information, des difficultés à interpréter correctement le reste des données présentes et des résultats biaisés notamment avec de larges sous-séquences absentes. Ainsi, ce travail de thèse se focalise sur la complétion de larges séquences manquantes dans les séries monovariées puis multivariées peu ou faiblement corrélées. Un premier axe de travail a été une recherche d'une requête similaire à la fenêtre englobant (avant/après) le trou. Cette approche est basée sur une comparaison de signaux à partir d'un algorithme d'extraction de caractéristiques géométriques (formes) et d'une mesure d'appariement élastique (DTW - Dynamic Time Warping). Un package R CRAN a été développé, DTWBI pour la complétion de série monovariée et DTWUMI pour des séries multidimensionnelles dont les signaux sont non ou faiblement corrélés. Ces deux approches ont été comparées aux approches classiques et récentes de la littérature et ont montré leur faculté de respecter la forme et la dynamique du signal. Concernant les signaux peu ou pas corrélés, un package DTWUMI a aussi été développé. Le second axe a été de construire une similarité floue capable de prender en compte les incertitudes de formes et d'amplitude du signal. Le système FSMUMI proposé est basé sur une combinaison floue de similarités classiques et un ensemble de règles floues. Ces approches ont été appliquées à des données marines et météorologiques dans plusieurs contextes : classification supervisée de cytogrammes phytoplanctoniques, segmentation non supervisée en états environnementaux d'un jeu de 19 capteurs issus d'une station marine MAREL CARNOT en France et la prédiction météorologique de données collectées au Vietnam. / Missing data are a prevalent problem in many domains of pattern recognition and signal processing. Most of the existing techniques in the literature suffer from one major drawback, which is their inability to process incomplete datasets. Missing data produce a loss of information and thus yield inaccurate data interpretation, biased results or unreliable analysis, especially for large missing sub-sequence(s). So, this thesis focuses on dealing with large consecutive missing values in univariate and low/un-correlated multivariate time series. We begin by investigating an imputation method to overcome these issues in univariate time series. This approach is based on the combination of shape-feature extraction algorithm and Dynamic Time Warping method. A new R-package, namely DTWBI, is then developed. In the following work, the DTWBI approach is extended to complete large successive missing data in low/un-correlated multivariate time series (called DTWUMI) and a DTWUMI R-package is also established. The key of these two proposed methods is that using the elastic matching to retrieving similar values in the series before and/or after the missing values. This optimizes as much as possible the dynamics and shape of knowledge data, and while applying the shape-feature extraction algorithm allows to reduce the computing time. Successively, we introduce a new method for filling large successive missing values in low/un-correlated multivariate time series, namely FSMUMI, which enables to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy grades of basic similarity measures and fuzzy logic rules. Finally, we employ the DTWBI to (i) complete the MAREL Carnot dataset and then we perform a detection of rare/extreme events in this database (ii) forecast various meteorological univariate time series collected in Vietnam

Water infrastructures have been implemented to support the vital activities of human society. The infrastructure developments at the same time have interrupted the natural catchment response characteristics, challenging society to implement effective water resources planning and management strategies. The Telemark area in southern Norway has seen a large number of water infrastructure developments, particularly hydropower, over more than a century. Recent developments in decision support tools for flood control and reservoir operation has raised the need to compute inflows from local catchments, most of which are regulated or have no observed data. This has contributed for the motivation of this PhD thesis work, with an aim of improving runoff estimation at ungauged catchments, and the research results are presented in four manuscript scientific papers.  The inverse distance weighting, inverse distance squared weighting, ordinary kriging, universal kriging and kriging with external drift were applied to analyse precipitation variability and estimate daily precipitation in the study area. The geostatistical based univariate and multivariate map-correlation concepts were applied to analyse and physically understand regional hydrological response patterns. The Sobol variance based sensitivity analysis (VBSA) method was used to investigate the HBV hydrological model parameterization significances on the model response variations and evaluate the model’s reliability as a prediction tool. The HBV hydrological model space transferability into ungauged catchments was also studied.  The analyses results showed that the inverse distance weighting variants are the preferred spatial data interpolation methods in areas where relatively dense precipitation station network can be found.  In mountainous areas and in areas where the precipitation station network is relatively sparse, the kriging variants are the preferred methods. The regional hydrological response correlation analyses suggested that geographic proximity alone cannot explain the entire hydrological response correlations in the study area. Besides, when the multivariate map-correlation analysis was applied, two distinct regional hydrological response patterns - the radial and elliptical-types were identified. The presence of these hydrological response patterns influenced the location of the best-correlated reference streamgauges to the ungauged catchments. As a result, the nearest streamgauge was found the best-correlated in areas where the radial-type hydrological response pattern is the dominant. In area where the elliptical-type hydrological response pattern is the dominant, the nearest reference streamgauge was not necessarily the best-correlated. The VBSA verified that varying up to a minimum of four to six influential HBV model parameters can sufficiently simulate the catchments' responses characteristics when emphasis is given to fit the high flows. Varying up to a minimum of six influential model parameters is necessary to sufficiently simulate the catchments’ responses and maintain the model performance when emphasis is given to fit the low flows. However, varying more than nine out of the fifteen HBV model parameters will not make any significant change on the model performance.  The hydrological model space transfer study indicated that estimation of representative runoff at ungauged catchments cannot be guaranteed by transferring model parameter sets from a single donor catchment. On the other hand, applying the ensemble based model space transferring approach and utilizing model parameter sets from multiple donor catchments improved the model performance at the ungauged catchments. The result also suggested that high model performance can be achieved by integrating model parameter sets from two to six donor catchments. Objectively minimizing the HBV model parametric dimensionality and only sampling the sensitive model parameters, maintained the model performance and limited the model prediction uncertainty.

Made available in DSpace on 2016-06-02T20:06:08Z (GMT). No. of bitstreams: 1 5213.pdf: 2943691 bytes, checksum: 6d301fea12ee3950f36c4359dd4a627e (MD5) Previous issue date: 2013-04-30 / Financiadora de Estudos e Projetos / The aim of this study was to investigate the monthly amount of unfavourable days to pollutant dispersion in the atmosphere on the metropolitan region of S ão Paulo (RMSP). It was considered two data sets derived from the air quality monitoring on the RMSP: (1) monthly observations of the times series of annual period and (2) monthly observations of the times series of period form May to September. It was used two classes of models: the Vector Autoregressive models (VAR) and Generalized Additive Models for Location, Scale and Shape (GAMLSS). The techniques presented in this dissertation was focus in: VAR class had emphasis on modelling stationary time series; and GAMLSS class had emphasis on models for count data, like Delaporte (DEL), Negative Binomial type I (NBI), Negative Binomial type II (NBII), Poisson (PO), inflated Poisson Zeros (ZIP), Inverse Poisson Gaussian (PIG) and Sichel (SI). The VAR was used only for the data set (1) obtaining a good prediction of the monthly amount of unfavourable days, although the adjustment had presented relatively large residues. The GAMLSS were used in both data sets, and the NBII model had good performance to data set (1), and ZIP model for data set (2). Also, it was made a simulation study to better understanding of the GAMLSS class for count data. The data were generated from three different Negative Binomial distributions. The results shows that the models NBI, NBII, and PIG adjusted well the data generated. The statistic techniques used in this dissertation was important to describe and understand the air quality problem. / O objetivo deste trabalho foi investigar a quantidade mensal de dias desfavoráveis à dispersão de poluentes na atmosfera da região metropolitana de São Paulo (RMSP). Foram considerados dois conjuntos de dados provenientes do monitoramento da qualidade do ar da RMSP: (1) um contendo observações mensais das séries temporais do período anual e (2) outro contendo observações mensais das séries temporais do período de maio a setembro. Foram utilizadas duas classes de modelos: os Modelos Vetoriais Autorregressivos (VAR) e os Modelos Aditivos Generalizados para Locação, Escala e Forma (GAMLSS), ressaltando que as técnicas apresentadas nessa dissertação da classe VAR têm ênfase na modelagem de séries temporais estacionárias e as da classe GAMLSS têm ênfase nos modelos para dados de contagem, sendo eles: Delaporte (DEL), Binomial Negativa tipo I (NBI), Binomial Negativa tipo II (NBII), Poisson (PO), Poisson Inflacionada de Zeros (ZIP), Poisson Inversa Gaussiana (PIG) e Sichel (SI). O modelo VAR foi utilizado apenas para o conjunto de dados (1), obtendo uma boa previsão da quantidade mensal de dias desfavoráveis, apesar do ajuste ter apresentado resíduos relativamente grandes. Os GAMLSS foram utilizados em ambos conjuntos de dados, sendo que os modelos NBII e ZIP melhor se ajustaram aos conjuntos de dados (1) e (2) respectivamente. Além disso, realizou-se um estudo de simulação para compreender melhor os GAMLSS investigados. Os dados foram gerados de três diferentes distribuições Binomiais Negativas. Os resultados obtidos mostraram que, tanto os modelos NBI e NBII como o modelo PIG, ajustaram bem os dados gerados. As técnicas estatísticas utilizadas nessa dissertação foram importantes para descrever e compreender o problema da qualidade do ar.

Ce document met l'accent sur la caractéristique des informations de la Chine dans l'environnement complexe de l'information, à la recherche empirique sur la relation entre l'information caractéristique de la Chine avec le marché boursier, outre d'étudier le modèle de diffusion de l'information concernant les différentes caractéristiques de la Chine sur le marché boursier. Les deux données transversales et série temporelles sont appliquées en mesurant la dépendance à partir du micro-indicateur, et en outre au niveau macro d'étudier la dépendance liée au phénomène unique de la Chine . Dans l'étude du niveau micro, nous choisissons le lien politique comme le mode variable de l'information de la Chine. Nous avons découvert que différents liens politiques ont donné lieu à différentes performances de l'entreprise, ce qui affecte plus le rendement des actions à la fois sur le temps et l'échelle du rendement anormal. Dans les tests de la dépendance au niveau macro, nous introduisons l'approche copula empirique dans l'immobilier, le marché de l'or et des actions. Notre résultat a détecté la dépendance univariée parmi les trois marchés bien qu'ils soient deux à deux indépendants. Enfin, nous avions établi un marché immobilier et boursier artificiel pour analyser la réaction du marché boursier avec la politique de l'immobilier basé sur les caractéristiques de la Chine. Basé sur la recherche ci-dessus, nous concluons que la caractéristique des informations de la Chine a un effet sur le marché financier tant au niveau micro et macro, et canalisés entre eux. Ainsi, nous devons tenir en compte ces caractéristiques dans l'étude de l'analyse du marché financier de la Chine. / After recent financial crisis, financial asset clustered and fell together, although there was not significant dependent relationship detected in academic research. lt is an indisputable fact that the correlation and dependence between financial asset and market is far more beyond our current knowledge. On stock market studies, in the current "Big data" world, the complexity and wide variety information calls for research on the particular kind of information and its effect on the stock market. Thus, we could further study the relationship and dependence among financial asset to detect the information diffusion pattern in financial market. To achieve this objective, exiting data sources and analytics required to be improved. This paper focuses on the China characteristic information in the complex information environment, to empirically research on the relationship of China characteristic information with stock market, and further study information diffusion pattern regarding to different China characteristics in stock market. Both cross-seclional and time series data are applied with measuring dependence from micro indicators, and further studied the on the macro level dependence related to China unique phenomenon. ln micro level study, we choose political connection as information which is particular China pattern. By non-parametric analysis, we conclude different political connections resulted in different stock performance. Then we considered stock analyst recommendations as aggregated information proxy, applying event study to test the stock reaction to information controlling for political connection and ratings. We discovered that different political connection affect stock retum both on the lime and scale of abnormal return. ln testing for macro level dependence, we introduce empirical copula approach with stock, real estate and gold market. Our result detected univariate dependence among the three market although they are pairwise independent. Finally, we constructed an agent-based artificial stock and housing market to test the stock market reaction with housing market policy based on China characteristics. Based on the above research, we conclude that China characteristic information do have effect on the financial market from both micro and macro level, and channeled between them. Thus, we need to consider these characteristic in studying China financial market issue.

Submitted by Izabel Franco (izabel-franco@ufscar.br) on 2016-09-27T13:57:54Z No. of bitstreams: 1 DissACM.pdf: 2683283 bytes, checksum: 013455f7d8c0d48a1566d18bcdd0fbe8 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2016-10-03T18:18:22Z (GMT) No. of bitstreams: 1 DissACM.pdf: 2683283 bytes, checksum: 013455f7d8c0d48a1566d18bcdd0fbe8 (MD5) / Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2016-10-03T18:18:38Z (GMT) No. of bitstreams: 1 DissACM.pdf: 2683283 bytes, checksum: 013455f7d8c0d48a1566d18bcdd0fbe8 (MD5) / Made available in DSpace on 2016-10-03T18:29:41Z (GMT). No. of bitstreams: 1 DissACM.pdf: 2683283 bytes, checksum: 013455f7d8c0d48a1566d18bcdd0fbe8 (MD5) Previous issue date: 2014-10-10 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / The main objective of this research is to provide statistical tools that allow the comparison of individuals in a speci ed sports category. Particularly, the present study is focused on the performance evaluation in football using univariate and multivariate methods. The univariate approach is given by Z-CELAFISCS methodology, which was developed with the purpose of identifying talents in the sport. The multivariate approaches are given by the construction of indicators, speci cally by means of principal component analysis, factor analysis and copulas. These indicators allows the reduction of the dimensionality of the data in studying, providing better interpretation of the results and improving comparability between the performance and assortment of individuals. To facilitate the use of the methodology studied here was built an online statistical system called i-Sports. / principal objetivo do trabalho é apresentar ferramentas estatísticas que permitam a comparação de indivíduos em uma determinada modalidade esportiva. Particularmente, o estudo exposto é voltado à avaliação de desempenho em futebol, utilizando métodos univariados e multivariados. A abordagem univariada é dada pela metodologia Z-CELAFISCS, desenvolvida com o propósito de identi car talentos no esporte. As abordagens multivariadas são dadas pela construção de indicadores, mais especi camente por meio da análise de componentes principais, análise fatorial e cópulas. A obtenção desses indicadores possibilita a redução da dimensionalidade do estudo, fornecendo melhor interpretação dos resultados e melhor comparabilidade entre o desempenho e rankeamento dos indivíduos. Para facilitar a utilização da metodologia aqui estudada foi construído um sistema estat ístico online chamado de i-Sports.

Submitted by ANA KARLA PEREIRA RODRIGUES (anakarla_@hotmail.com) on 2017-08-09T15:33:48Z No. of bitstreams: 1 arquivototal.pdf: 4699110 bytes, checksum: ef3b7c0aa5c4758d2c77e65ad6a81ad3 (MD5) / Made available in DSpace on 2017-08-09T15:33:48Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 4699110 bytes, checksum: ef3b7c0aa5c4758d2c77e65ad6a81ad3 (MD5) Previous issue date: 2016-06-20 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This work involves the development of two studies that are presented in chapters 2 and 3. At first, a new method to perform the calibration transfer was designed. This method was developed to make use of separate variables instead of using the full spectrum or spectral windows. To accomplish this task a univariate procedure is initially used to correct the spectra recorded in the secondary equipment, given a set of transfer samples. A robust regression technique is then used to obtain a model with small sensitivity with respect to the univariate correction. The proposed method is employed in two case studies involving near infrared spectrometric determination of specific mass, research octane number and naphtenes in gasoline, and moisture and oil in corn. In both cases, better calibration transfer results were obtained in comparison with piecewise direct standardization (PDS). In the second, a new strategy for cotton seed classification using near infrared (NIR) hyperspectral images (HSI) was developed. Initially the cotton seeds samples were recorded on a station HSI image-NIR and a conventional spectrometer NIR. Thereon, the images were segmented and the mean spectrum of each seed was extract. Classification models SPA-LDA e PLS-DA based on the mean spectral were developed for two data sets. The results for models SPA-LDA and PLSDA showed that the classification with HSI-NIR data set has been achieved with greater accuracy when compared to models for the NIR-conventional data set. / Este trabalho envolve o desenvolvimento de dois estudos, que são apresentados nos capítulos 2 e 3. No primeiro, um novo método para realizar a transferência de calibração foi concebido. Este método foi desenvolvido para fazer uso de variáveis isoladas em vez de usar todo o espectro ou janelas espectrais. Para realizar essa tarefa, um procedimento univariado é inicialmente usado para corrigir os espectros registrados no equipamento secundário, dado um conjunto de amostras de transferência. Uma técnica de regressão robusta é então usada para obter um modelo com pequena sensibilidade em relação aos resíduos da correção univariada. O novo método é então empregado em dois estudos de caso envolvendo análise espectrométrica NIR, em que foram determinados os parâmetros massa específica, RON (Research Octane Number) e teor de naftênicos em gasolina e os teores de água e óleo em amostras de milho. Os resultados do novo método foram melhores do que os obtidos usando o método PDS. No segundo, uma nova estratégia para classificação de sementes de algodão usando imagens hiperespectrais no NIR foi desenvolvido. Inicialmente as amostras de sementes de algodão foram registradas em uma estação de imagem HSI-NIR e em um equipamento NIR convencional. Após isso, as imagens foram segmentadas e os espectros médios de cada semente foram extraídos. Os modelos de classificação SPA-LDA e PLS-DA baseados nos espectros médios foram construídos para os dois conjuntos de dados. Os resultados SPA-LDA e PLS-DA para os modelos demonstraram que a classificação com os dados HSI-NIR foi alcançada com maior exatidão quando comparada aos modelos obtidos usando o NIR-convencional.

Le travail présenté dans cette thèse s’inscrit dans le cadre de la modélisation d’images texturées à l’aide des représentations multi-échelles et multi-orientations. Partant des résultats d’études en neurosciences assimilant le mécanisme de la perception humaine à un schéma sélectif spatio-fréquentiel, nous proposons de caractériser les images texturées par des modèles probabilistes associés aux coefficients des sous-bandes. Nos contributions dans ce contexte concernent dans un premier temps la proposition de différents modèles probabilistes permettant de prendre en compte le caractère leptokurtique ainsi que l’éventuelle asymétrie des distributions marginales associées à un contenu texturée. Premièrement, afin de modéliser analytiquement les statistiques marginales des sous-bandes, nous introduisons le modèle Gaussien généralisé asymétrique. Deuxièmement, nous proposons deux familles de modèles multivariés afin de prendre en compte les dépendances entre coefficients des sous-bandes. La première famille regroupe les processus à invariance sphérique pour laquelle nous montrons qu’il est pertinent d’associer une distribution caractéristique de type Weibull. Concernant la seconde famille, il s’agit des lois multivariées à copules. Après détermination de la copule caractérisant la structure de la dépendance adaptée à la texture, nous proposons une extension multivariée de la distribution Gaussienne généralisée asymétrique à l’aide de la copule Gaussienne. L’ensemble des modèles proposés est comparé quantitativement en terme de qualité d’ajustement à l’aide de tests statistiques d’adéquation dans un cadre univarié et multivarié. Enfin, une dernière partie de notre étude concerne la validation expérimentale des performances de nos modèles à travers une application de recherche d’images par le contenu textural. Pour ce faire, nous dérivons des expressions analytiques de métriques probabilistes mesurant la similarité entre les modèles introduits, ce qui constitue selon nous une troisième contribution de ce travail. Finalement, une étude comparative est menée visant à confronter les modèles probabilistes proposés à ceux de l’état de l’art. / In this thesis we study the statistical modeling of textured images using multi-scale and multi-orientation representations. Based on the results of studies in neuroscience assimilating the human perception mechanism to a selective spatial frequency scheme, we propose to characterize textures by probabilistic models of subband coefficients.Our contributions in this context consist firstly in the proposition of probabilistic models taking into account the leptokurtic nature and the asymmetry of the marginal distributions associated with a textured content. First, to model analytically the marginal statistics of subbands, we introduce the asymmetric generalized Gaussian model. Second, we propose two families of multivariate models to take into account the dependencies between subbands coefficients. The first family includes the spherically invariant processes that we characterize using Weibull distribution. The second family is this of copula based multivariate models. After determination of the copula characterizing the dependence structure adapted to the texture, we propose a multivariate extension of the asymmetric generalized Gaussian distribution using Gaussian copula. All proposed models are compared quantitatively using both univariate and multivariate statistical goodness of fit tests. Finally, the last part of our study concerns the experimental validation of the performance of proposed models through texture based image retrieval. To do this, we derive closed-form metrics measuring the similarity between probabilistic models introduced, which we believe is the third contribution of this work. A comparative study is conducted to compare the proposed probabilistic models to those of the state-of-the-art.

Foreign exchange market is the largest financial market in the world and thus prediction of foreign exchange rate values is of interest to millions of people. In this research, I evaluated the performance of Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Autoregressive Integrated Moving Average (ARIMA) and Moving Average (MA) on the USD/CAD and USD/AUD exchange pairs for 1-day, 1-week and 2-weeks predictions. For LSTM and GRU, twelve macroeconomic indicators along with past exchange rate values were used as features using data from January 2001 to December 2019. Predictions from each model were then integrated with uncertainty modeling to find out the chance of a model’s prediction being greater than or less than a user-defined target value using the error distribution from the test dataset, Monte-Carlo simulation trials and ChancCalc excel add-in. Results showed that ARIMA performs slightly better than LSTM and GRU for 1-day predictions for both USD/CAD and USD/AUD exchange pairs. However, when the period is increased to 1-week and 2-weeks, LSTM and GRU outperform both ARIMA and moving average for both USD/CAD and USD/AUD exchange pair.