The increased online presence and use of technology by today’s adolescents has created new places where bullying can occur. The aim of this thesis is to specify a prediction model that can accurately predict the risk of cyberbullying victimization. The data used is from a survey conducted at five secondary schools in Pereira, Colombia. A logistic regression model with random effects is used to predict cyberbullying exposure. Predictors are selected by lasso, tuned by cross-validation. Covariates included in the study includes demographic variables, dietary habit variables, parental mediation variables, school performance variables, physical health variables, mental health variables and health risk variables such as alcohol and drug consumption. Included variables in the final model are demographic variables, mental health variables and parental mediation variables. Variables excluded in the final model includes dietary habit variables, school performance variables, physical health variables and health risk variables. The final model has an overall prediction accuracy of 88%.

Signal Detection Theory (SDT; Green & Swets, 1966) is a well-established technique to analyze accuracy data in a number of experimental paradigms in psychology, most notably memory and perception, by separating a response bias/criterion from the theoretically bias-free discriminability/sensitivity. As SDT has traditionally been applied, the researcher may be confronted with loss in statistical power and erroneous inferences. A generalized linear mixed-effects modeling (GLMM) approach is presented and advantages with regard to power and precision are demonstrated with an example analysis. Using this approach, a correlation of response bias and sensitivity was detected in the dataset, especially prevalent at the item level, though a correlation between these measures is usually not found to be reported in the memory literature. Directions for future extensions of the method as well as a brief discussion of the correlation between response bias and sensitivity are enclosed. / Graduate / 2019-03-22

Watching oneself on video (self-observation) compared to self-observation coupled with a skilled model video (mixed-models) was examined in a gymnastics environment to determine whether combining two model types would be better than just one. Twenty-one gymnasts learned one gymnastics skill with mixed-models and a second skill with self-observation across pre-test, three learning sessions, and post-test. Physical performance, scored by two evaluators, revealed a significant condition by session interaction (F(3,51) = 3.329, p = .027). At session 3 and post-test, scores obtained with mixed-models were significantly higher than those with self-observation. Cognitive representation of the skills was measured at pre-test and post-test via error detection and recognition tests, analyzed using signal detection. Participants had significantly higher response sensitivity scores with mixed-models (F(1,14) = 10.810, p = .005) compared to self-observation. The conclusion drawn is that it is better to incorporate self and skilled models in a gymnastics setting than self-observation alone.

The Secondary Prevention in Uppsala Primary Health Care Project (SUPRIM) is a randomized controlled trial evaluating the effects of cognitive behavioral therapy on coronary heart disease patients. Various outcomes of psychological and physical health are recorded every six months approximately, over the course of two years after entry to the trial. In this thesis, relationships between the psychological outcome variables, Stress, Anxiety, Depression and Exhaustion, and five physical health biomarkers, are assessed using bivariate linear mixed models. Significant associations are found between one of the biomarkers and both Depression and Exhaustion, and also between one of the other biomarkers and Exhaustion.

We propose to extend the use of penalized likelihood based variable selection methods to hierarchical generalized linear models (HGLMs) for jointly modellingboth the mean and variance structures. We are interested in applying these newmethods on multilevel structured data, hence we assume a two-level hierarchical structure, with subjects nested within groups. We consider a generalized linearmixed model (GLMM) for the mean, with a structured dispersion in the formof a generalized linear model (GLM). In the first instance, we model the varianceof the random effects which are present in the mean model, or in otherwords the variation between groups (between-level variation). In the second scenario,we model the dispersion parameter associated with the conditional varianceof the response, which could also be thought of as the variation betweensubjects (within-level variation). To do variable selection, we use the smoothlyclipped absolute deviation (SCAD) penalty, a penalized likelihood variable selectionmethod, which shrinks the coefficients of redundant variables to 0 and at thesame time estimates the coefficients of the remaining important covariates. Ourmethods are likelihood based and so in order to estimate the fixed effects in ourmodels, we apply iterative procedures such as the Newton-Raphson method, inthe form of the LQA algorithm proposed by Fan and Li (2001). We carry out simulationstudies for both the joint models for the mean and variance of the randomeffects, as well as the joint models for the mean and dispersion of the response,to assess the performance of our new procedures against a similar process whichexcludes variable selection. The results show that our method increases both theaccuracy and efficiency of the resulting penalized MLEs and has 100% successrate in identifying the zero and non-zero components over 100 simulations. Forthe main real data analysis, we use the Health Survey for England (HSE) 2004dataset. We investigate how obesity is linked to several factors such as smoking,drinking, exercise, long-standing illness, to name a few. We also discover whetherthere is variation in obesity between individuals and between households of individuals,as well as test whether that variation depends on some of the factorsaffecting obesity itself.

A avaliação da predição de um modelo por meio do cálculo do seu risco esperado é uma importante etapa no processo de escolha do um preditor eficiente para observações futuras. Porém, deve ser evitado nessa avaliação usar a mesma base em que foi criado o preditor, pois traz, no geral, estimativas abaixo do valor real do risco esperado daquele modelo. As técnicas de validação cruzada (K-fold, Leave-One-Out, Hold-Out e Bootstrap) são aconselhadas nesse caso, pois permitem a divisão de uma base em amostra de treino e validação, fazendo assim que a criação do preditor e a avaliação do seu risco sejam feitas em bases diferentes. Este trabalho apresenta uma revisão dessas técnicas e suas particularidades na estimação do risco esperado. Essas técnicas foram avaliadas em dois modelos mistos com distribuições Normal e Logístico e seus desempenhos comparados por meio de estudos de simulação. Por fim, as metodologias foram aplicadas em um conjunto de dados real. / The appraisal of models prediction through the calculation of the expected risk is an important step on the process of the choice of an efficient predictor to future observations. However, in this evaluation it should be avoided to use the same data to calculate the predictor on which it was created, due to it brings, in general, estimates above the real expected risk value of the model. In this case, the cross-validation methods (K-fold, Leave-One-Out, Hold-Out and Bootstrap) are recommended because the partitioning of the data in training and validation samples allows the creation of the predictor and its risk evaluation on different data sets. This work presents a briefing of this methods and its particularities on the expected risk estimation. These methods were evaluated on two mixed models with Normal and Logistic distributions and their performances were compared through simulation cases. Lastly, those methods were applied on a real database.

Resting metabolic rate (RMR) represents the lowest level of aerobic metabolism in a resting individual. By contrast, maximal metabolic rate (MMR) reflects the upper limit of aerobic metabolism achieved during intensive exercise. As RMR and MMR define the boundaries of the possible levels of metabolism expressed by a normothermic individual, a key question is whether RMR and MMR are correlated. To evaluate the relationship between RMR and MMR, I took repeated paired measurements of RMR and MMR on 165 white-footed mice (Peromyscus leucopus) during the summer of 2018. Repeatability (R±se) was significant for both RMR and MMR (RRMR=0.15±0.07 and RMMR=0.27±0.12). At the residual level (within-individual), RMR and MMR were significantly and positively correlated (re=0.20, 95% confidence intervals: 0.04, 0.34). Such a positive residual correlation could be result of correlated phenotypic plasticity. By contrast, RMR and MMR were significantly and negatively correlated at the among-individual level (rind=-0.87, 95% confidence intervals: -0.99, -0.28). The negative among-individual correlation suggests there are trade-offs between the maintenance and active components of the energy budget (allocation model). Future research should investigate the relationship between RMR and other energetically expensive behaviours and activities to understand how energy is allocated among individuals.

Results from prospective studies examining associations between sleep duration and weight gain have been mixed. Melatonin has been hypothesized to mediate the association between sleep duration and weight/body composition. In cross-sectional studies, aMT6s has been shown to be inversely associated with weight/body fat percentage. We examined associations between baseline sleep duration, insomnia status, aMT6s levels with weight/body fat percentage through 6 years, utilizing a subset 690 women who participated in a breast cancer case-control study nested within the WHI-OS. Multi-variable and mixed-effects regression was used to calculate beta-coefficients and 95% confidence intervals. Cross-sectional analyses showed urinary aMT6s levels were inversely associated with BMI and body fat percentage. No associations were observed between sleep patterns and measures of adiposity. The prospective relationship between urinary aMT6 levels and weight/body fat percentage was complex. Age-adjusted mixed models show an association in the interaction term between year and aMT6s with body fat percentage (βinteraction:0.09, pinteraction:0.16, p=0.07), demonstrating the influence of baseline aMT6s and time on changes in outcome. Women with higher baseline aMT6s had a trajectory of increased body fat percentage and weight gain steepest between baseline and year 3, whereas women with lower baseline aMT6 levels had a trajectory of decreased body fat percent and weight between year 3 and year 6. The prospective association between melatonin levels and adiposity measures was unexpected. Future studies with objective measures of sleep and repeated measures of melatonin may shed light of possible explanations for our findings.

Nous nous intéressons à desméthodes statistiques pour estimer l'héritabilitéd'un caractère biologique, qui correspond à lapart des variations de ce caractère qui peut êtreattribuée à des facteurs génétiques. Nousproposons dans un premier temps d'étudierl'héritabilité de traits biologiques continus àl'aide de modèles linéaires mixtes parcimonieuxen grande dimension. Nous avons recherché lespropriétés théoriques de l'estimateur du maximumde vraisemblance de l'héritabilité : nousavons montré que cet estimateur était consistantet vérifiait un théorème central limite avec unevariance asymptotique que nous avons calculéeexplicitement. Ce résultat, appuyé par des simulationsnumériques sur des échantillons finis,nous a permis de constater que la variance denotre estimateur était très fortement influencéepar le ratio entre le nombre d'observations et lataille des effets génétiques. Plus précisément,quand le nombre d’observations est faiblecomparé à la taille des effets génétiques (ce quiest très souvent le cas dans les étudesgénétiques), la variance de l’estimateur était trèsgrande. Ce constat a motivé le développementd'une méthode de sélection de variables afin dene garder que les variants génétiques les plusimpliqués dans les variations phénotypiques etd’améliorer la précision des estimations del’héritabilité.La dernière partie de cette thèse est consacrée àl'estimation d'héritabilité de données binaires,dans le but d'étudier la part de facteursgénétiques impliqués dans des maladies complexes.Nous proposons d'étudier les propriétésthéoriques de la méthode développée par Golanet al. (2014) pour des données de cas-contrôleset très efficace en pratique. Nous montronsnotamment la consistance de l’estimateur del’héritabilité proposé par Golan et al. (2014). / We study statistical methods toestimate the heritability of a biological trait,which is the proportion of variations of thistrait that can be explained by genetic factors.First, we propose to study the heritability ofquantitative traits using high-dimensionalsparse linear mixed models. We investigate thetheoretical properties of the maximumlikelihood estimator for the heritability and weshow that it is a consistent estimator and that itsatisfies a central limit theorem with a closedformexpression for the asymptotic variance.This result, supported by an extendednumerical study, shows that the variance of ourestimator is strongly affected by the ratiobetween the number of observations and thesize of the random genetic effects. Moreprecisely, when the number of observations issmall compared to the size of the geneticeffects (which is often the case in geneticstudies), the variance of our estimator is verylarge. This motivated the development of avariable selection method in order to capturethe genetic variants which are involved themost in the phenotypic variations and providemore accurate heritability estimations. Wepropose then a variable selection methodadapted to high dimensional settings and weshow that, depending on the number of geneticvariants actually involved in the phenotypicvariations, called causal variants, it was a goodidea to include or not a variable selection stepbefore estimating heritability.The last part of this thesis is dedicated toheritability estimation for binary data, in orderto study the proportion of genetic factorsinvolved in complex diseases. We propose tostudy the theoretical properties of the methoddeveloped by Golan et al. (2014) for casecontroldata, which is very efficient in practice.Our main result is the proof of the consistencyof their heritability estimator.

Indiana University-Purdue University Indianapolis (IUPUI) / A popular non-invasive brain activity measurement method is based on the functional magnetic resonance imaging (fMRI). Such data are frequently used to study functional connectivity (FC) defined as statistical association among two or more anatomically distinct fMRI signals (Friston, 1994). FC has emerged in recent years as a valuable tool for providing a deeper understanding of neurodegenerative diseases and neuropsychiatric disorders, such as Alzheimer's disease and autism. Information about complex association structure in high-dimensional fMRI data is often discarded by a calculating an average across complex spatiotemporal processes without providing an uncertainty measure around it. First, we propose a non-parametric approach to estimate the uncertainty of dynamic FC (dFC) estimates. Our method is based on three components: an extension of a boot strapping method for multivariate time series, recently introduced by Jentsch and Politis (2015); sliding window correlation estimation; and kernel smoothing. Second, we propose a two-step approach to analyze and summarize dFC estimates from a task-based fMRI study of social-to-heavy alcohol drinkers during stimulation with avors. In the first step, we apply our method from the first paper to estimate dFC for each region subject combination. In the second step, we use semiparametric additive mixed models to account for complex correlation structure and model dFC on a population level following the study's experimental design. Third, we propose to utilize the estimated dFC to study the system's modularity defined as the mutually exclusive division of brain regions into blocks with intra-connectivity greater than the one obtained by chance. As a result, we obtain brain partition suggesting the existence of common functionally-based brain organization. The main contribution of our work stems from the combination of the methods from the fields of statistics, machine learning and network theory to provide statistical tools for studying brain connectivity from a holistic, multi-disciplinary perspective.