This study uses latent class analysis and latent transition analysis to model and compare patterns of offending over time for males and females by: (1) identifying qualitative dimensions of offending; (2) modeling how patterns of offending change over time; and (3) exploring factors related to patterns of offending. This is a secondary analysis of data from the Edinburgh Study of Youth Transition and Crime, a longitudinal study consisting of a cohort of about 4,000 young people from secondary schools in the City of Edinburgh who responded to questionnaires administered between 1988 and 2001, when they were about 12, 13, 14, and 15 years old. Previous studies of offending have used trajectory modeling to explore the course of offending from onset to termination, but the models are generally based on a count of types of offences aggregated across individuals over time, making it difficult to determine whether individuals exhibit more versatility or specialization in offending or switch offences from one point in time to another. In addition, most of the studies on patterns of offending have focused primarily on males. An understanding of patterns of offending over time for both males and females is important for the design and selection of developmentally appropriate prevention/treatment strategies. The present study adds to the literature by (1) further exploring the small and understudied literature on offence transitions; (2) examining more closely the development of female offending separately from and in relation to male offending; and (3) exploring a range of factors (criminogenic and non-criminogenic) related to the development of offending for both males and females. While the same number of qualitative dimensions (latent classes) characterised male and female offending in this study, there were some structural differences. There was also evidence of shifts in the qualitative dimensions for males and females over time. Finally, those factors classified as criminogenic were more likely to differentiate among the latent classes than those classified as non-criminogenic.

In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters determining the probability law of the latent process is carried out by a new MCMC method called permutation sampling. In a first run we use the random permutation sampler to sample from the unconstrained posterior. We will demonstrate that a lot of important information, such as e.g. estimates of the subject-specific regression coefficients, is available from such an unidentified model. The MCMC output of the random permutation sampler is explored in order to find suitable identifiability constraints. In a second run we use the permutation sampler to sample from the constrained posterior by imposing identifiablity constraints. The unknown number of classes is determined by formal Bayesian model comparison through exact model likelihoods. We apply a new method of computing model likelihoods for latent class models which is based on the method of bridge sampling. The approach is applied to simulated data and to data from a metric conjoint analysis in the Austrian mineral water market. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Sentiment analysis aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text, and has received a rapid growth of interest in natural language processing in recent years. Probabilistic topic models, on the other hand, are capable of discovering hidden thematic structure in large archives of documents, and have been an active research area in the field of information retrieval. The work in this thesis focuses on developing topic models for automatic sentiment analysis of web data, by combining the ideas from both research domains. One noticeable issue of most previous work in sentiment analysis is that the trained classifier is domain dependent, and the labelled corpora required for training could be difficult to acquire in real world applications. Another issue is that the dependencies between sentiment/subjectivity and topics are not taken into consideration. The main contribution of this thesis is therefore the introduction of three probabilistic topic models, which address the above concerns by modelling sentiment/subjectivity and topic simultaneously. The first model is called the joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. Unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when applied to new domains, the weakly-supervised nature of JST makes it highly portable to other domains, where the only supervision information required is a domain-independent sentiment lexicon. Apart from document-level sentiment classification results, JST can also extract sentiment-bearing topics automatically, which is a distinct feature compared to the existing sentiment analysis approaches. The second model is a dynamic version of JST called the dynamic joint sentiment-topic (dJST) model. dJST respects the ordering of documents, and allows the analysis of topic and sentiment evolution of document archives that are collected over a long time span. By accounting for the historical dependencies of documents from the past epochs in the generative process, dJST gives a richer posterior topical structure than JST, and can better respond to the permutations of topic prominence. We also derive online inference procedures based on a stochastic EM algorithm for efficiently updating the model parameters. The third model is called the subjectivity detection LDA (subjLDA) model for sentence-level subjectivity detection. Two sets of latent variables were introduced in subjLDA. One is the subjectivity label for each sentence; another is the sentiment label for each word token. By viewing the subjectivity detection problem as weakly-supervised generative model learning, subjLDA significantly outperforms the baseline and is comparable to the supervised approach which relies on much larger amounts of data for training. These models have been evaluated on real world datasets, demonstrating that joint sentiment topic modelling is indeed an important and useful research area with much to offer in the way of good results.

Bakgrund: Överrapportering av patienter mellan vårdenheter är en vanligt förekommande företeelse för sjuksköterskor. Det finns en tydlig risk att patientsäkerheten påverkas negativt om inte överrapporteringen till fullo uppfyller sitt syfte att överföra korrekt information till mottagaren. Syfte: Syftet med denna kvalitativa studie var att belysa sjuksköterskors upplevelse av överrapportering inom akutverksamhet. Metod: Tolv sjuksköterskor, verksamma på fyra olika kliniker inom den akuta verksamheten intervjuades. Genom latent innehållsanalys identifierades huvudtemat överrapportering - ett komplext samspel. Detta tema kunde därefter delas upp i tre subteman, vilka delades upp i åtta kategorier. Resultat: I resultatet framträdde överrapportering i en akutverksamhet som ett komplext samspel mellan givare och mottagare. Det uppkom flera faktorer på både individ- och organissationsnivå som påverkade överrapporteringen. Även faktorer hos patienten, såsom dennes tillstånd, upplevdes påverka överrapporteringen. Respondenterna var  tydliga med att överrapportering av patienter var en viktig men riskfylld del av handläggningen och att det var väsentligt att det som ansågs relevant information för patienten gick fram till mottagaren utan att fördröja tiden till behandling. Vikten av en struktur såsom genom en fastställd mall eller en egen framtagen struktur ansågs viktigt för att patientsäkra överrapporteringen. Slutsats: Vårt resultat styrks av tidigare forskning där överrapporteringen ses som ett riskmoment. Vikten av en struktur för att säkerställa att information kommer fram samt hur viktigt fokus hos både givare och mottagare är framkom. Studien bidrar med aspekten att det som anses som väsentlig information varierar beroende på var i vårdkedjan patienten befinner sig.

<p>Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.</p><p>Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.</p> / Dissertation

<p>The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme that ties the three areas is Bayesian model emulation: solving challenging analysis/computational problems using creative model emulators. This idea defines theoretical and applied advances in non-linear, non-Gaussian state-space modeling, dynamic sparsity, decision analysis and statistical computation, across linked contexts of multivariate time series and dynamic networks studies. Examples and applications in financial time series and portfolio analysis, macroeconomics and internet studies from computational advertising demonstrate the utility of the core methodological innovations.</p><p>Chapter 1 summarizes the three areas/problems and the key idea of emulating in those areas. Chapter 2 discusses the sequential analysis of latent threshold models with use of emulating models that allows for analytical filtering to enhance the efficiency of posterior sampling. Chapter 3 examines the emulator model in decision analysis, or the synthetic model, that is equivalent to the loss function in the original minimization problem, and shows its performance in the context of sequential portfolio optimization. Chapter 4 describes the method for modeling the steaming data of counts observed on a large network that relies on emulating the whole, dependent network model by independent, conjugate sub-models customized to each set of flow. Chapter 5 reviews those advances and makes the concluding remarks.</p> / Dissertation

Objectives We first sought to assess if bicyclist typology was associated with health. Second, we investigated whether bicyclist typology was related to health through physical activity and commute bicycling. Finally, we sought to develop profiles of disposition toward commute bicycling following proposed changes to a specific destination and the significance of pertinent covariates. Methods Data from the 2014 Georgia State University-Bicycling Survey were used. We first estimated the adjusted odds of worse health-related quality of life by bicyclist typology. A mediation model was then used to estimate the relative total and direct effects of bicyclist typology on health-related quality of life and relative indirect effects through physical activity and commute bicycling. A finite mixture modeling approach was used to identify latent classes of disposition toward whether proposed changes to a specific destination would increase likelihood of commute bicycling. The manual 3-Step protocol was used to assess the effect of covariates on the probability of latent class membership. Results Respondents who had never bicycled, were not motivated to commute bicycle, and who required greater bicycle facilities to feel comfortable commute bicycling had higher odds of worse health-related quality of life. Physical activity and, to a lesser extent, commute bicycling status mediated the effect of bicyclist typology on health-related quality of life. The seven-class solution was decided on as the “best” model for disposition toward whether proposed destination improvements would increase the likelihood of commute bicycling. Several covariates were identified that impact the probability of latent class assignment. Conclusions Initial evidence of a health disparity by bicyclist typology was revealed. Physical activity appears to serve as the primary means through which bicyclist typology has an effect on health. Urban environments that make physical activity, including commute bicycling, more comfortable for a larger proportion of the population may be a potential important health intervention. Understanding the patterns of disposition toward whether proposed destination improvements would increase the likelihood of commute bicycling may assist in targeting and prioritizing commute bicycling-related interventions toward subpopulations of interest.

Background: Migrant farmworkers are among the highest-risk populations for latent TB infection (LTBI) in the United States with numerous barriers to healthcare access and increased vulnerability to infectious diseases. LTBI is usually diagnosed on the border using the tuberculin skin test (TST). QuantiFERON-TB Gold In-Tube (QFT-GIT) also measures immune response against specific Mycobacterium tuberculosis antigens. The objective of this study is to assess the comparability of TST and QFT-GIT to detect LTBI among migrant farmworkers on the border, as well as to examine the effects of various demographic and clinical factors on test positivity. Methods: Participants were recruited using mobile clinics on the San Luis US-Mexico border and tested with QFT-GIT and TST. Demographic profiles and clinical histories were collected. Kappa coefficients assessed agreement between TST and QFT-GIT using various assay cutoffs. Logistic regression examined factors associated with positive TST or QFT-GIT results. Results: Of 109 participants, 59 of 108 (55 %) were either TST (24/71, 34 %) or QFT-GIT (52/106, 50 %) positive. Concordance between TST and QFT-GIT was fair (71 % agreement,kappa= 0.38, 95 % CI: 0.15, 0.61). Factors associated with LTBI positivity included smoking (OR = 1.26, 95 % CI-1.01-1.58) and diabetes/high blood sugar (OR = 0.70, 95 % CI = 0.51-0.98). Discussion: Test concordance between the two tests was fair, with numerous discordant results observed. Greater proportion of positives detected using QFT-GIT may help avoid LTBI under-diagnosis. Assessment of LTBI status on the border provides evidence whether QFT-GIT should replace the TST in routine practice, as well as identifies risk factors for LTBI among migrant populations.

Background: Frailty, a state indicating vulnerability to poor health outcomes, is a common condition in later life. However, research and intervention progress is hindered by the current lack of a consensus frailty definition and poor understanding of relationships between frailty and depression. Objectives: The goal of this research is to understand the interrelationships between frailty and depression among older adults. Specifically, this project aims 1) to examine the construct overlap between depression and three definitions of frailty (biological syndrome, medical burdens, and functional domains), 2) to determine the degree to which this overlap varies by age, gender, race/ethnicity and other individual characteristics, 3) to evaluate how the association between frailty and depression influences prediction of adverse health outcomes. Methods: This project uses data from the 2004-2012 Health and Retirement Study (HRS), an ongoing, nationally-representative cohort study of adults over the age of 55. Frailty was indexed by three alternative conceptual models: 1) biological syndrome, 2) cumulative medical burdens, and 3) functional domains. Depressive symptoms were indexed by the 8-item Center for Epidemiologic Studies Depression (CESD) scale. Latent class analysis and confirmatory factor analysis were used to assess the construct overlap between depressive symptoms and frailty. Latent growth curve modeling were used to evaluate associations between frailty and depression, and to estimate their joint influence on two adverse health outcomes: nursing home admission and falls. Results: The measurement overlap of frailty and depression was high using a categorical latent variable approach. Approximately 73% of individuals with severe depressive symptoms, and 85% of individuals with primarily somatic depressive symptoms, were categorized as concurrently frail. When modeled as continuous latent factors, each of the three frailty latent factors was significantly correlated with depression: biological syndrome (ρ = .67, p <.01); functional domains (ρ = .70, p <.01); and medical burdens (ρ = .62, p <.01). Higher latent frailty trajectories were associated with higher likelihood of experiencing nursing home admission and serious falls. This association with adverse health outcomes was attenuated after adjustment for depression as a time-varying covariate. Conclusions: Findings suggest that frailty and frailty trajectories are potentially important indicators of vulnerability to adverse health outcomes. Future investigations of frailty syndrome, however it is operationalized, should account for its substantial association with depression in order to develop more accurate measurement and effective treatment.

Latent variable models (LVMs) are commonly used in the scenario where the outcome of the main interest is an unobservable measure, associated with multiple observed surrogate outcomes, and affected by potential risk factors. This thesis develops an approach of efficient handling missing surrogate outcomes and covariates in two- and three-level latent variable models. However, corresponding statistical methodologies and computational software are lacking efficiently analyzing the LVMs given surrogate outcomes and covariates subject to missingness in the LVMs. We analyze the two-level LVMs for longitudinal data from the National Growth of Health Study where surrogate outcomes and covariates are subject to missingness at any of the levels. A conventional method for efficient handling of missing data is to reexpress the desired model as a joint distribution of variables, including the surrogate outcomes that are subject to missingness conditional on all of the covariates that are completely observable, and estimate the joint model by maximum likelihood, which is then transformed to the desired model. The joint model, however, identifies more parameters than desired, in general. The over-identified joint model produces biased estimates of LVMs so that it is most necessary to describe how to impose constraints on the joint model so that it has a one-to-one correspondence with the desired model for unbiased estimation. The constrained joint model handles missing data efficiently under the assumption of ignorable missing data and is estimated by a modified application of the expectation-maximization (EM) algorithm.