Resilience is defined as doing well despite significant hardships. Based on four principles informing a social-ecological definition of resilience (that is, decentrality, complexity, a typicality, and cultural relativity), Ungar (2011, 2012) hypothesised an explanation of social-ecological resilience. Seen from this perspective, resilience involves active youthsocial-ecological transactions towards meaningful, resilience-promoting supports. Youths’ usage of these supports might differ due to, among others, specific lived experiences, contextual influences, and youths’ subjective perceptions. While Ungar’s explanation is both popular and plausible, it has not been quantitatively tested, also not in South Africa. Moreover, there is little quantitatively informed evidence about youths’ differential resource-use, particularly when youth share a context and culture, and how such knowledge might support social ecologies to facilitate resilience processes. The overall purpose of this study was, therefore, to investigate black South African youths’ resilience processes from a social-ecological perspective, using a sample of black South African youth. This purpose was operationalised as sub-aims (explained below) that addressed the aforementioned gaps in theory. Data to support this study were accessed via the Pathways to Resilience Research Project (see www.resilienceresearch.org), of which this study is part. The Pathways to Resilience Research Project investigates the social-ecological contributions to youths’ resilience across cultures. This study consists of three manuscripts. Using a systematic literature review, Manuscript 1 evaluated how well quantitative studies of South African youth resilience avoided the pitfalls made public in the international critiques of resilience studies. For the most part, quantitative studies of South African youth resilience did not mirror international developments of understanding resilience as a complex socio-ecologically facilitated process. The results identified aspects of quantitative studies of South African youth resilience that necessitated attention. In addition, the manuscript called for quantitative studies that would statistically explain the complex dynamic resilience-supporting transactions between South African youths and their contexts. Manuscript 2 answered the aforementioned call by grounding its research design in a theoretical framework that respected the sociocultural life-worlds of South African youth (that is, Ungar’s Social-Ecological Explanation of Resilience). Ungar’s Social-Ecological Explanation of Resilience was modelled using latent variable modelling in Mplus 7.2, with data gathered with the Pathways to Resilience Youth Measure by 730 black South African school-going youth. The results established that South African youths adjusted well to challenges associated with poverty and violence because of resilience processes that were co-facilitated by social ecologies. It was, furthermore, concluded that school engagement was a functional outcome of the resilience processes among black South African youth. Manuscript 2 also provided evidence that an apposite, necessary, and respectful education contributed towards schooling as a meaningful resource. Manuscript 3 provided deeper insight into aspects of black South African youths’ resilience processes. Manuscript 3 investigated youths’ self-reported perceptions of resilience-promoting resources by means of data gathered by the Pathways to Resilience Youth Measure. Consequently, two distinct groups of youth from the same social ecology made vulnerable by poverty were compared (that is, functionally resilient youth, n = 221; and formal service-using youth, n = 186). Measurement invariance, latent mean differences in Mplus 7.2, and analyses of variance in SPSS 22.0 were employed. What emerged was that positive perceptions of caregiving (that is, physical and psychological) were crucial to youths’ use of formal resilience-promoting resources and subsequent functional outcomes. The conclusions resulted in implications for both caregivers and practitioners. / PhD (Educational Psychology) North-West University, Vaal Triangle Campus 2015

Resilience is defined as doing well despite significant hardships. Based on four principles informing a social-ecological definition of resilience (that is, decentrality, complexity, a typicality, and cultural relativity), Ungar (2011, 2012) hypothesised an explanation of social-ecological resilience. Seen from this perspective, resilience involves active youthsocial-ecological transactions towards meaningful, resilience-promoting supports. Youths’ usage of these supports might differ due to, among others, specific lived experiences, contextual influences, and youths’ subjective perceptions. While Ungar’s explanation is both popular and plausible, it has not been quantitatively tested, also not in South Africa. Moreover, there is little quantitatively informed evidence about youths’ differential resource-use, particularly when youth share a context and culture, and how such knowledge might support social ecologies to facilitate resilience processes. The overall purpose of this study was, therefore, to investigate black South African youths’ resilience processes from a social-ecological perspective, using a sample of black South African youth. This purpose was operationalised as sub-aims (explained below) that addressed the aforementioned gaps in theory. Data to support this study were accessed via the Pathways to Resilience Research Project (see www.resilienceresearch.org), of which this study is part. The Pathways to Resilience Research Project investigates the social-ecological contributions to youths’ resilience across cultures. This study consists of three manuscripts. Using a systematic literature review, Manuscript 1 evaluated how well quantitative studies of South African youth resilience avoided the pitfalls made public in the international critiques of resilience studies. For the most part, quantitative studies of South African youth resilience did not mirror international developments of understanding resilience as a complex socio-ecologically facilitated process. The results identified aspects of quantitative studies of South African youth resilience that necessitated attention. In addition, the manuscript called for quantitative studies that would statistically explain the complex dynamic resilience-supporting transactions between South African youths and their contexts. Manuscript 2 answered the aforementioned call by grounding its research design in a theoretical framework that respected the sociocultural life-worlds of South African youth (that is, Ungar’s Social-Ecological Explanation of Resilience). Ungar’s Social-Ecological Explanation of Resilience was modelled using latent variable modelling in Mplus 7.2, with data gathered with the Pathways to Resilience Youth Measure by 730 black South African school-going youth. The results established that South African youths adjusted well to challenges associated with poverty and violence because of resilience processes that were co-facilitated by social ecologies. It was, furthermore, concluded that school engagement was a functional outcome of the resilience processes among black South African youth. Manuscript 2 also provided evidence that an apposite, necessary, and respectful education contributed towards schooling as a meaningful resource. Manuscript 3 provided deeper insight into aspects of black South African youths’ resilience processes. Manuscript 3 investigated youths’ self-reported perceptions of resilience-promoting resources by means of data gathered by the Pathways to Resilience Youth Measure. Consequently, two distinct groups of youth from the same social ecology made vulnerable by poverty were compared (that is, functionally resilient youth, n = 221; and formal service-using youth, n = 186). Measurement invariance, latent mean differences in Mplus 7.2, and analyses of variance in SPSS 22.0 were employed. What emerged was that positive perceptions of caregiving (that is, physical and psychological) were crucial to youths’ use of formal resilience-promoting resources and subsequent functional outcomes. The conclusions resulted in implications for both caregivers and practitioners. / PhD (Educational Psychology) North-West University, Vaal Triangle Campus 2015

Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. This thesis, in its general form, proposes different techniques within the frameworks of two learning systems for representation and modeling. Namely, latent support vector machines (latent SVMs) and deep learning. First, we propose various approaches to group the positive samples into clusters of visually similar instances. Given a fixed representation, the sampled space of the positive distribution is usually structured. The proposed clustering techniques include a novel similarity measure based on exemplar learning, an approach for using additional annotation, and augmenting latent SVM to automatically find clusters whose members can be reliably distinguished from background class.  In another effort, a strongly supervised DPM is suggested to study how these models can benefit from privileged information. The extra information comes in the form of semantic parts annotation (i.e. their presence and location). And they are used to constrain DPMs latent variables during or prior to the optimization of the latent SVM. Its effectiveness is demonstrated on the task of animal detection. Finally, we generalize the formulation of discriminative latent variable models, including DPMs, to incorporate new set of latent variables representing the structure or properties of negative samples. Thus, we term them as negative latent variables. We show this generalization affects state-of-the-art techniques and helps the visual recognition by explicitly searching for counter evidences of an object presence. Following the resurgence of deep networks, in the last works of this thesis we have focused on deep learning in order to produce a generic representation for visual recognition. A Convolutional Network (ConvNet) is trained on a largely annotated image classification dataset called ImageNet with $\sim1.3$ million images. Then, the activations at each layer of the trained ConvNet can be treated as the representation of an input image. We show that such a representation is surprisingly effective for various recognition tasks, making it clearly superior to all the handcrafted features previously used in visual recognition (such as HOG in our first works on DPM). We further investigate the ways that one can improve this representation for a task in mind. We propose various factors involving before or after the training of the representation which can improve the efficacy of the ConvNet representation. These factors are analyzed on 16 datasets from various subfields of visual recognition. / <p>QC 20160908</p>

No description available.

Ma thèse développe deux parties. La première considère que la psychologie expérimentale doit clarifier son positionnement épistémologique pour clarifier la validation formelle de sa démarche, sans forcément devoir se référencer au cadre de la Science Physique. A partir d’une réflexion critique, je propose de décaler le cadre épistémologique en psychologie et de poser clairement un cadre pragmatique-réaliste. La thèse essentielle défendue dans ce travail est : 1/ les propriétés mentales doivent être comprises comme des phénomènes émergents, ce qui implique que leurs analyses ne peuvent se faire ni au niveau neuronal, ni au niveau de la dynamique interne de processus cognitifs, mais nécessairement au niveau de ces phénomènes émergents ; 2/ pour analyser les propriétés mentales comme formes émergentes, la psychométrie a besoin d’user de concepts qui sont en tension permanente entre une objectivité et une intersubjectivité ; en conséquence, la psychométrie doit affirmer une démarche pragmatiste-réaliste, en rupture avec l’empirisme-réaliste classique ; 3/ une approche pragmatiste-réaliste, basée entre autre sur l’abduction, permet de dépasser les contradictions pointées dans la littérature académique sur les propriétés mentales et leurs mesures ; 4/ un cadre de mesure de propriétés mentales par des variables latentes devient dès lors possible si ce cadre est compris lui aussi comme pragmatiste-réaliste ; 5/ mais ce recours au pragmatisme-réaliste renvoie en conséquence une critique à la fois des modèles avec variables latentes développés dans la littérature académique et les usages sociaux de ces modèles. La seconde partie de ma thèse porte sur un cadre particulier de formalisation des variables latentes : le cadre formatif. Je développe des simulations Monte Carlo pour vérifier le spectre des paramètres permettant une mesure formative efficiente dans le cadre d’un positionnement réaliste-empirique. / My thesis considers that experimental psychology must clarify its epistemological position to clarify the formal validation of its approach, without necessarily having to refer to the framework of Science Physics. From a critical reflection, I propose to shift the epistemological framework in psychology and clearly pose a pragmatic-realistic framework. The main thesis of this work is: 1 / mental properties must be understood as emerging phenomena, which implies that their analysis can not be done nor at the neuronal level, nor at the internal dynamics of cognitive processes, but necessarily at these emerging phenomena; 2 / to analyze the mental properties as emerging forms, psychometrics need to use concepts that are in permanent tension between objectivity and intersubjectivity; accordingly, psychometrics must assert a pragmatic-realist approach, breaking with classical empiricism-realistic; 3 / a pragmatist-realistic approach, based among other things on the abduction, can overcome the contradictions pointed in the academic literature on mental properties and their measurements; 4 / a framework for measuring mental properties by latent variables becomes possible if the framework is also understood as a pragmatic-realist; 5 / but use realistic-pragmatic returns accordingly critical of both models with latent variables developed in the academic literature and the social uses of these models. The second part of my thesis focuses on a specific part of formalization of latent variables: the formative model. I develop Monte Carlo simulations to check the range of parameters for efficient formative measure as part of a realistic-empirical positioning.

Em um contexto de analise de dados de sobrevivência univariados ou multivariados, dados de tempos de falha caracterizam-se pela possibilidade de poderem ser censurados. Embora comum na pratica, a censura impede o uso de alguns procedimentos estatisticos covencionais o que vem motivando, em especial apos a publicacao do artigo de Cox (1972), o desenvolvimento de metodos estatisticos nessa area. Uma linha de estudo recente e a de que, em algumas situacoes, a variavel resposta esteja sendo inuenciada por variaveis latentes, variaveis estas que sao usadas, em um sentido estatistico, para descreverem efeitos geneticos ou ambientais compartilhados pelos indivduos ou, ainda, covariaveis nao consideradas no estudo. Nesse trabalho, enfase e dada aos modelos de sobrevivencia que consideram tempos de falha multivariados e variaveis latentes. Esses tempos aparecem quando, por exemplo, cada individuo em estudo esta sujeito a diversos eventos ou, quando existe um agrupamento natural ou artificial o qual induz dependencia entre os tempos dos individuos do mesmo grupo. Modelos com variaveis latentes em que tais tempos de falha ocorrem em intervalos de tempo, ou seja, em um contexto de censura intervalar sao especialmente considerados nesse trabalho. O modelo de fragilidade gama para dados de sobrevivencia com censura intervalar e proposto, nesse trabalho, como um criterio para a selecao de bovinos. Como uma alternativa para esta selecao, o modelo de curvas de crescimento com efeitos aleatorios e tambem considerado. Para a estimacao dos parametros envolvidos em ambos os modelos propostos, programas computacionais sao apresentados. Uma abordagem Bayesiana e considerada no processo de estimação sendo, o metodo de Markov chain Monte Carlo (MCMC) utilizado e as distribuicoes a posteriori obtidas, usando-se o amostrador de Gibbs. O modelo de fragilidade gama com censura intervalar e o de curvas de crescimento com efeitos aleatorios sao comparados por meio de um estudo de simulação. Para ilustrar ambos os modelos propostos, estudos com bovinos das racas Nelore e Canchim são utilizados. / In a context of univariate or multivariate survival data analysis, failure times data are characterized by the possibility to be censored. Although common in practice, censoring precludes the use of some conventional statistical procedures and it has been motivating, specially after the publication of the Cox's paper (1972), the development of statistical methods in this area. A recent topic of study is concerned with some situations where the response variable is in uenced by latent variables which are used in a statistical sense to describe genetic or environmental efects shared by individuals or also covariates not considered in the study. In this work emphasis is given to survival models which consider multivariate failure times and latent variables. Such times occur when, for instance, each individual under study is exposed to several events or when there is a natural or artificial clustering that causes dependence among times of those individuals at the same cluster. Models with latent variables where such failure times lie in intervals of time, i.e. in an interval censored context are specially considered in this work. The gamma frailty interval censored survival model is proposed in this work as a selection criterion for cattle. As an alternative selection criterion the growth curves model with random efects is also considered. To estimate the involved parameters in both proposed models, computational programs are presented. A Bayesian approach is considered in the estimation process so that the Markov chain Monte Carlo (MCMC) method is used and the posterior distributions are obtained using Gibbs sampling. The gamma frailty interval-censored survival model and the growth curves model with random efects are compared using a simulation study. To illustrate both proposed models studies with Nelore and Canchim cattle are used.

L’utilisation des mesures subjectives en épidémiologie s’est intensifiée récemment, notamment avec la volonté de plus en plus affirmée d’intégrer la perception qu’ont les sujets de leur santé dans l’étude des maladies et l’évaluation des interventions. La psychométrie regroupe les méthodes statistiques utilisées pour la construction des questionnaires et l’analyse des données qui en sont issues. Ce travail de thèse avait pour but d’explorer différents problèmes méthodologiques soulevés par l’utilisation des techniques psychométriques en épidémiologie. Trois études empiriques sont présentées et concernent 1/ la phase de validation de l’instrument : l’objectif était de développer, à l’aide de données simulées, un outil de calcul de la taille d’échantillon pour la validation d’échelle en psychiatrie ; 2/ les propriétés mathématiques de la mesure obtenue : l’objectif était de comparer les performances de la différence minimale cliniquement pertinente d’un questionnaire calculée sur des données de cohorte, soit dans le cadre de la théorie classique des tests (CTT), soit dans celui de la théorie de réponse à l’item (IRT) ; 3/ son utilisation dans un schéma longitudinal : l’objectif était de comparer, à l’aide de données simulées, les performances d’une méthode statistique d’analyse de l’évolution longitudinale d’un phénomène subjectif mesuré à l’aide de la CTT ou de l’IRT, en particulier lorsque certains items disponibles pour la mesure différaient à chaque temps. Enfin, l’utilisation de graphes orientés acycliques a permis de discuter, à l’aide des résultats de ces trois études, la notion de biais d’information lors de l’utilisation des mesures subjectives en épidémiologie. / Recently, subjective measurements have increasingly been used in epidemiology, alongside the growing will to integrate individuals’ point of view on their health in studies on diseases or health interventions. Psychometrics includes statistical methods used to develop questionnaires and to analyze questionnaire data. This doctoral dissertation aimed to explore methodological issues raised by the use of psychometric techniques in epidemiology. Three empirical studies are presented and cover 1 / the validation stage of a questionnaire: the objective was to develop, using simulated data, a tool to determine sample size for internal validity studies on psychiatric scale; 2 / the mathematical properties of the subjective measurement: the objective was to compare the performances of the minimal clinically important difference of a questionnaire, assessed on data from a cohort study, computed using the classical test theory (CTT) framework or the item response theory framework (IRT); 3 / its use in a longitudinal design: the objective was to compare, using simulated data, the performances of a statistical method aimed to analyze the longitudinal course of a subjective phenomenon measured using the CTT or IRT framework, especially when some of the available items used for its measurement differ at each time of data collection. Finally, directed acyclic graphs were used to discuss the results from these three studies and the concept of information bias when subjective measurements are used in epidemiology.

Neste trabalho apresentamos os mais importantes processos de estimação em algumas classes de modelos de resposta ao item (Dicotômicos e Policotômicos). Discutimos algumas propriedades desses métodos. Com o objetivo de comparar o desempenho dos métodos conduzimos simulações apropriadas. / In this work we show the most important estimation methods for some item response models (both dichotomous and polichotomous). We discuss some proprieties of these methods. To compare the characteristic of these methods we conducted appropriate simulations.

This thesis investigates long-term cognitive outcome in two cohorts of adolescents and young adults exposed to stressors during the perinatal period: one group born preterm (&lt;37 weeks of gestation and birth weight &lt;1,500 g); one group exposed to two or more courses of antenatal corticosteroids (ACS), to stimulate lung maturation in the face of threatening preterm birth. In fetal life the brain undergoes dramatic growth, and a disruption to the early establishment of functional neural networks may interrupt development in ways that are difficult to predict. Executive function refers to a set of cognitive processes that are important for purposeful regulation of thought, emotion, and behavior, and even a subtle depreciation may influence overall functioning. Study I investigated the stability of executive function development after preterm birth. Executive functions were differentiated into working memory and cognitive flexibility. Both components were highly stable from preschool age to late adolescence. In Study II, we identified subgroups within the group of children born preterm with respect to cognitive profiles at 5½ and 18 years, and identified longitudinal streams. Outcome after preterm birth was diverse, and insufficiently predicted by perinatal and family factors. Individuals performing at low levels at 5½ years were unlikely to improve over time, while a group of individuals performing at or above norm at 5½ years had improved their performance relative to term-born peers by age 18. Studies I and II pointed to the need for developmental monitoring of those at risk, prior to formal schooling. Study III investigated long-term cognitive outcome after repeat ACS treatment. The study did not provide support for the concern that repeat ACS exposure will have an adverse impact on cognitive function later in life. In sum, exposure to perinatal stressors resulted in great variation in outcome. However, for many, their rough beginnings had not left a lasting mark. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 1: Submitted.</p>

We develop statistical methods for tackling two important problems in genetic association studies. First, we propose a Bayesian approach to overcome the winner's curse in genetic studies. Second, we consider a Bayesian latent variable model for analyzing longitudinal family data with pleiotropic phenotypes. Winner's curse in genetic association studies refers to the estimation bias of the reported odds ratios (OR) for an associated genetic variant from the initial discovery samples. It is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in the low power studies. We investigate the performance of the method with simulations and applications to four real data examples. Pleiotropy occurs when a single genetic factor influences multiple quantitative or qualitative phenotypes, and it is present in many genetic studies of complex human traits. The longitudinal family studies combine the features of longitudinal studies in individuals and cross-sectional studies in families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest. We propose a Bayesian latent variable modeling approach to model multiple phenotypes simultaneously in order to detect the pleiotropic effect and allow for longitudinal and/or family data. An efficient MCMC algorithm is developed to obtain the posterior samples by using hierarchical centering and parameter expansion techniques. We apply spike and slab prior methods to test whether the phenotypes are significantly associated with the latent disease status. We compute Bayes factors using path sampling and discuss their application in testing the significance of factor loadings and the indirect fixed effects. We examine the performance of our methods via extensive simulations and apply them to the blood pressure data from a genetic study of type 1 diabetes (T1D) complications.