As current educational policies continue to emphasize the importance of college readiness and growth, it is essential to understand the degree to which test scores collected throughout middle school and high school can provide information to make valid inferences about students' college readiness. This thesis sought to summarize the college readiness of Iowa students, describe the nature of student growth, and clarify the relationship between student growth and college readiness. Together, the results support the validity argument that scores from a general achievement test can be used for measuring student growth and making on-track interpretations about college readiness. Results of analyses on the use of benchmarks as indicators of college readiness are presented first. The analyses showed that the state's general achievement test was just as accurate as the ACT when the criterion was defined by grades in domain-specific, credit-bearing courses. Next, latent growth models and growth mixture models were used to summarize and evaluate longitudinal changes in student achievement and their relationship with college outcomes. A calibration sample representing potential college-bound students was used to set the growth trajectories. Then a cohort of students representing the full student population was used to provide validity evidence in support of the growth trajectories. It was shown that students in the highest-performing group could be considered college ready. Several applications of the growth models are also presented. The typical performance on a variety of college outcomes for each developmental group was presented for the validation sample. A second application illustrated how individual patterns of growth in Grade 8 could be used to predict future class membership in Grade 11. This thesis was predicated on the notion that understanding and documenting the nature of student growth, the college readiness of Iowa students, and the relationship between the two is an important step in improving the college readiness of Iowa students and meeting the future needs of an aligned K-16 educational system. As this study is among the first to examine the relationship between college readiness and student growth using modern latent variable modeling techniques with actual college outcomes, guidelines for future research are presented.

Child maltreatment is a pervasive social and public health problem in the United States. The negative effects of child maltreatment can include poor mental and relational health outcomes. The experience of discrimination has been shown to have many of the same mental and relational health difficulties. Child maltreatment and discrimination are both social health problems that disproportionately affect the most marginalized people in our society (people of color, people with disabilities, LGBT individuals). Complex trauma, or the experience of multiple traumas, has been shown to have worse mental and relational health outcomes then experiencing one type of trauma alone. Feminist theory is a useful framework for studying how those with marginalized identities experience the effects of child maltreatment. Feminist theory argues that it is essential to incorporate an analysis of power to truly capture the experience of complex trauma for people with marginalized identities. The effects of child maltreatment and discrimination have been studied individually, however little is known about the effects of experiencing both. Data from the National Survey of Midlife Development in the United States (MIDUS) biomarker project was used to examine the effect of experiencing both child maltreatment and discrimination. Latent profile analysis was used to create distinct profiles of trauma out of child maltreatment variables and discrimination. A four profile solution was determined to be the best fitting model. The profiles were Low Trauma, Child Maltreatment/Discrimination, Child Maltreatment and Child Maltreatment/ Discrimination High. Analysis of co-variance was then used to determine how each profile of trauma was related to anxiety, depression, family support and family strain. Differences were found among the profiles and the mental health and relational outcomes. Results and clinical implications are discussed.

Purpose: This study investigates one possible reason for individual differences in vocabulary learning from written context. A Latent Semantic Analysis (LSA) model is used to motivate the prediction of a causal relationship between semantic knowledge for words in a text and the quality of their hypotheses about the semantics of novel words, an effect mediated by reading comprehension. The purpose of this study was to test this prediction behaviorally, using a within subject repeated measures design to control for other variables affecting semantic word learning. Methods: Participants in 6th grades (n=23) were given training to increase semantic knowledge of words from one of two texts, counterbalanced across participants. After training, participants read untreated and treated texts, which contained six nonword forms. Measures were taken of reading comprehension (RC) and the quality of the readers' hypotheses about the semantics of the novel words (HSNW). Text difficulty and semantic informativeness of the texts about nonwords were controlled. Results: All participants had increases in semantic knowledge of taught words after intervention. For the group as a whole, RC scores were significantly higher in the treated than untreated condition, but HSNW scores were not significantly higher in the treated than untreated condition. Reading comprehension ability was a significant moderator of the effect of treatment on HSNW. A subgroup of participants with lower scores on a standardized reading comprehension measure (n=6) had significantly higher HSNW and RC scores in the treated than untreated condition. Participants with higher standardized reading comprehension scores (n=17) showed no effect of treatment on either RC or HSNW. Difference scores for RC and difference scores for HSNW were strongly related, indicating that within subjects, there is a relationship between RC and HSNW. Conclusions: The results indicate that for a subgroup of readers with weaker reading comprehension, intervention to enhance lexical semantic richness had a substantial and significant effect on both their reading comprehension and on the quality of hypotheses that they generated about the meanings of novel words. Neither effect was found for a subgroup of readers with stronger reading comprehension. Clinical and educational implications are discussed.

Les modèles linéaires latents sont des modèles statistique puissants pour extraire la structure latente utile à partir de données non structurées par ailleurs. Ces modèles sont utiles dans de nombreuses applications telles que le traitement automatique du langage naturel et la vision artificielle. Pourtant, l'estimation et l'inférence sont souvent impossibles en temps polynomial pour de nombreux modèles linéaires latents et on doit utiliser des méthodes approximatives pour lesquelles il est difficile de récupérer les paramètres. Plusieurs approches, introduites récemment, utilisent la méthode des moments. Elles permettent de retrouver les paramètres dans le cadre idéalisé d'un échantillon de données infini tiré selon certains modèles, mais ils viennent souvent avec des garanties théoriques dans les cas où ce n'est pas exactement satisfait. Dans cette thèse, nous nous concentrons sur les méthodes d'estimation fondées sur l'appariement de moment pour différents modèles linéaires latents. L'utilisation d'un lien étroit avec l'analyse en composantes indépendantes, qui est un outil bien étudié par la communauté du traitement du signal, nous présentons plusieurs modèles semiparamétriques pour la modélisation thématique et dans un contexte multi-vues. Nous présentons des méthodes à base de moment ainsi que des algorithmes pour l'estimation dans ces modèles, et nous prouvons pour ces méthodes des résultats de complexité améliorée par rapport aux méthodes existantes. Nous donnons également des garanties d'identifiabilité, contrairement à d'autres modèles actuels. C'est une propriété importante pour assurer leur interprétabilité. / Latent linear models are powerful probabilistic tools for extracting useful latent structure from otherwise unstructured data and have proved useful in numerous applications such as natural language processing and computer vision. However, the estimation and inference are often intractable for many latent linear models and one has to make use of approximate methods often with no recovery guarantees. An alternative approach, which has been popular lately, are methods based on the method of moments. These methods often have guarantees of exact recovery in the idealized setting of an infinite data sample and well specified models, but they also often come with theoretical guarantees in cases where this is not exactly satisfied. In this thesis, we focus on moment matchingbased estimation methods for different latent linear models. Using a close connection with independent component analysis, which is a well studied tool from the signal processing literature, we introduce several semiparametric models in the topic modeling context and for multi-view models and develop moment matching-based methods for the estimation in these models. These methods come with improved sample complexity results compared to the previously proposed methods. The models are supplemented with the identifiability guarantees, which is a necessary property to ensure their interpretability. This is opposed to some other widely used models, which are unidentifiable.

Direct marketing problems have been intensively reviewed in the marketing literature recently, such as purchase frequency and time, sales profit, and brand choices. However, modeling the customer response, which is an important issue in direct marketing research, remains a significant challenge. This thesis is an empirical study of predicting customer response to direct marketing and applies a Bayesian approach, including the Bayesian Binary Regression (BBR) and the Hierarchical Bayes (HB). Other classical methods, such as Logistic Regression and Latent Class Analysis (LCA), have been conducted for the purpose of comparison. The results of comparing the performance of all these techniques suggest that the Bayesian methods are more appropriate in predicting direct marketing customer responses. Specifically, when customers are analyzed as a whole group, the Bayesian Binary Regression (BBR) has greater predictive accuracy than Logistic Regression. When we consider customer heterogeneity, the Hierarchical Bayes (HB) models, which use demographic and geographic variables for clustering, do not match the performance of Latent Class Analysis (LCA). Further analyses indicate that when latent variables are used for clustering, the Hierarchical Bayes (HB) approach has the highest predictive accuracy.

Cette thèse regroupe des contributions méthodologiques à l'analyse statistique des bases de données de pharmacovigilance. Les difficultés de modélisation de ces données résident dans le fait qu'elles produisent des matrices souvent creuses et de grandes dimensions. La première partie des travaux de cette thèse porte sur la classification croisée du tableau de contingence de pharmacovigilance à l’aide du modèle des blocs latents de Poisson normalisé. L'objectif de la classification est d'une part de fournir aux pharmacologues des zones intéressantes plus réduites à explorer de manière plus précise, et d'autre part de constituer une information a priori utilisable lors de l'analyse des données individuelles de pharmacovigilance. Dans ce cadre, nous détaillons une procédure d'estimation partiellement bayésienne des paramètres du modèle et des critères de sélection de modèles afin de choisir le modèle le plus adapté aux données étudiées. Les données étant de grandes dimensions, nous proposons également une procédure pour explorer de manière non exhaustive mais pertinente, l'espace des modèles en coclustering. Enfin, pour mesurer la performance des algorithmes, nous développons un indice de classification croisée calculable en pratique pour un nombre de classes élevé. Les développements de ces outils statistiques ne sont pas spécifiques à la pharmacovigilance et peuvent être utile à toute analyse en classification croisée. La seconde partie des travaux de cette thèse porte sur l'analyse statistique des données individuelles, plus nombreuses mais également plus riches en information. L'objectif est d'établir des classes d'individus selon leur profil médicamenteux et des sous-groupes d'effets et de médicaments possiblement en interaction, palliant ainsi le phénomène de coprescription et de masquage que peuvent présenter les méthodes existantes sur le tableau de contingence. De plus, l'interaction entre plusieurs effets indésirables y est prise en compte. Nous proposons alors le modèle des blocs latents multiple qui fournit une classification croisée simultanée des lignes et des colonnes de deux tableaux de données binaires en leur imposant le même classement en ligne. Nous discutons des hypothèses inhérentes à ce nouveau modèle et nous énonçons des conditions suffisantes de son identifiabilité. Ensuite, nous présentons une procédure d'estimation de ses paramètres et développons des critères de sélection de modèles associés. De plus, un modèle de simulation numérique des données individuelles de pharmacovigilance est proposé et permet de confronter les méthodes entre elles et d'étudier leurs limites. Enfin, la méthodologie proposée pour traiter les données individuelles de pharmacovigilance est explicitée et appliquée à un échantillon de la base française de pharmacovigilance entre 2002 et 2010. / This thesis gathers methodological contributions to the statistical analysis of large datasets in pharmacovigilance. The pharmacovigilance datasets produce sparse and large matrices and these two characteritics are the main statistical challenges for modelling them. The first part of the thesis is dedicated to the coclustering of the pharmacovigilance contingency table thanks to the normalized Poisson latent block model. The objective is on the one hand, to provide pharmacologists with some interesting and reduced areas to explore more precisely. On the other hand, this coclustering remains a useful background information for dealing with individual database. Within this framework, a parameter estimation procedure for this model is detailed and objective model selection criteria are developed to choose the best fit model. Datasets are so large that we propose a procedure to explore the model space in coclustering, in a non exhaustive way but a relevant one. Additionnally, to assess the performances of the methods, a convenient coclustering index is developed to compare partitions with high numbers of clusters. The developments of these statistical tools are not specific to pharmacovigilance and can be used for any coclustering issue. The second part of the thesis is devoted to the statistical analysis of the large individual data, which are more numerous but also provides even more valuable information. The aim is to produce individual clusters according their drug profiles and subgroups of drugs and adverse effects with possible links, which overcomes the coprescription and masking phenomenons, common contingency table issues in pharmacovigilance. Moreover, the interaction between several adverse effects is taken into account. For this purpose, we propose a new model, the multiple latent block model which enables to cocluster two binary tables by imposing the same row ranking. Assertions inherent to the model are discussed and sufficient identifiability conditions for the model are presented. Then a parameter estimation algorithm is studied and objective model selection criteria are developed. Moreover, a numeric simulation model of the individual data is proposed to compare existing methods and study its limits. Finally, the proposed methodology to deal with individual pharmacovigilance data is presented and applied to a sample of the French pharmacovigilance database between 2002 and 2010.

The world is ever increasing in its energy usage, making energy that is sustainable and secure harder to achieve. To fulfil the Paris agreement to limit global warming, the world needs to transition from fossil fuels toward more renewable energy sources, like wind and solar, but these sources have fluctuation in supply which often create a mismatch with demand. To combat this issue, thermal energy storage can be utilized, and one such technology is latent heat thermal energy storage. This study aimed to investigate the potential of latent heat thermal energy storage by developing a simple model of such a system and studying its impact on Rocklunda’s sport facilities. The model was developed by using MATLAB, primarily using the photovoltaic overproduction of the facilities to store as energy for the latent heat thermal energy storage. The implemented storage, based on the model’s result, had overall positive impact on the facilities. The optimized storage capacity was about 510 kWh, which throughout the storage’s lifetime would save ~4 989 MWh worth of heat by using the best performing phase change material: aluminium-silicon. The storage would also be able to utilize ~82% of the annual photovoltaic overproduction that would otherwise be unused/sold as well as reducing the heat demand by ~12% by using the heat stored via the storage. The implementation also proved to have beneficial effects on the environment as the saved heat was the equivalent of mitigating ~304 ton of CO2 emissions. Furthermore, there is a profit of ~236 000 SEK. / Reduction and Reuse of energy with interconnected Distribution and Demand (R2D2)

No description available.

The significance of credit risk models has increased with the introduction of new Basel accord known as Basel II. The aim of this study is default rate modeling. This thesis follows the two possible approaches of a macro credit risk modeling. First, empirical models are investigated. Second, a latent factor model based on Merton's idea is introduced. Both of these models are derived from individual default probability models. We employed data over the time period from 1988 to 2003 of the Finnish economy in the first part of this thesis. Time series of bankruptcy and firm's numbers were used. Aggregate data for whole economy as well as industry specific data were available. First, linear vector autoregressive models was used in case of dynamic empirical model. We examined how significant macroeconomic indicators determined the default rate in the whole economy and in the industry specific sector. However these models cannot provide microeconomic foundation as latent factor models. We employed a one- factor model in our estimation although, multi-factor models were also considered. A one-factor model was estimated using disaggregated industrial data. This estimation can help understand relation between credit risk and macroeconomic indicators. Obtained results were used in the second part of this...

Indiana University-Purdue University Indianapolis (IUPUI) / Rates of vaping among high school students have increased significantly over the past decade. Prior research has found significant associations between youth vaping and substance use. However, little is known about how vaping is associated with various patterns of polysubstance use and asthma in youth. We aimed to identify how youth are best categorized into classes based on co-occurring vaping and polysubstance use behaviors, how these classes are associated with youth background and demographic characteristics, and if these classes significantly predict asthma outcomes. Our sample consisted of nationally representative data from the 2017 and 2019 waves of the Youth Risk Behavior Survey (N = 28,442). We utilized Latent Class Analysis, multinomial logistic regression analyses, and binary logistic regression analyses to examine relationships between youth vaping, polysubstance use, and asthma. Three latent classes of substance use were identified: Polysubstance Users, Lifetime Alcohol and Vape Users, and Abstainers. Age, gender, grade and race were all significantly associated with odds of membership in the Polysubstance Users class, compared to the Abstainers class. Sexual identity was not associated with class membership. Membership in the Polysubstance Users class was significantly associated with higher odds of asthma, as compared to membership in the other two classes. These findings indicate that recent vaping is associated with high probabilities of recent polysubstance use. They also suggest that youth with high probabilities of vaping and polysubstance use are at significantly high risk for asthma compared to other classes vi of youth users and non-users. We recommend that future youth intervention strategies be tailored differently toward different classes of substance use and vaping. Future research should examine how the classes of vaping and substance use that we identify emerge in youth and what social factors (e.g., peer behavior, parental connectedness, etc.) influence their development.