Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 48 for “"Longitudinal Data Analysis"”.
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Analyzing Incomplete Longitudinal Binary Data Using Approximate Likelihood Methods
… in the outcome variable complicates the longitudinal data analysis, as it is necessary to incorporate the missing data model into the observed data likelihood function. We investigate two approximate likelihood methods, bivariate pseudo-likelihood (BPL) of Sinha et al. (2011) and …
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Variable screening and graphical modeling for ultra-high dimensional longitudinal data
… procedures are based on single replicate data and are not applicable to longitudinal data. This motivates us to propose a new Sure Independence Screening (SIS) procedure to bring the dimension from ultra-high down to a relatively large scale which is similar to or smaller than the sample …
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LOCAL LABOR MARKETS EXPOSURE TO ARTIFICIAL INTELLIGENCE
… a local labor market level. The study leveraged longitudinal data analysis to measure the effect of AI exposure on changes to employment at an occupational level from 2010-2019 in San Diego County, California. By applying this exploratory methodology, the study yielded several noteworthy …
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Peripheral Refractive Error and its Association with Myopia Development and Progression. An examination of the role that peripheral retinal defocus may play in the origin and progression of myopia
… (10.21 ±0.94 years old) at baseline and 286 longitudinally. At baseline, myopic children (n=61) had relative peripheral hyperopia at all eccentricities at distance and near, except at 10°-superior retina where relative peripheral myopia was observed at near. Hyperopic eyes displayed relative …
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Methods for the estimation of missing values in time series
Time Series is a sequential set of data measured over time. Examples of time series arise in a variety of areas, ranging from engineering to economics. The analysis of time series data constitutes an important area of statistics. Since, the data are records taken through time, missing observations …
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Canonical Correlation and Correspondence Analysis of Longitudinal Data
… study these relationships. Canonical correlation analysis (CCA) is a general multivariate method that is mainly used to study relationships when both sets of variables are quantitative. When the variables are qualitative (categorical), a technique called correspondence analysis (CA) is used. …
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Analysis of Models for Longitudinal and Clustered Binary Data
… dissertation deals with modeling and statistical analysis of longitudinal and clustered binary data. Such data consists of observations on a dichotomous response variable generated from multiple time or cluster points, that exhibit either decaying correlation or equi-correlated dependence. The …
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A "Holy Grail" of Work and Family Life? When and How Schedule Control Functions as a Resource
… life and health. Three patterns derived from longitudinal data analysis complicate the characterization of schedule control as solely a job resource. First, in Chapter 2 I reveal some of the downsides of schedule control for the work-family interface. I find that increases in schedule control …
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Returning to the Path. A hermeneutic phenomenological study of parental expectations and the meaning of transition to early parenting in couples with a pregnancy conceived using in-vitro fertilisation.
… phenomenological study using in-depth data analysis. Three couples expecting their first child, a singleton non-donor pregnancy conceived using IVF, were purposively selected and interviewed on three occasions: at 34 weeks pregnant, six weeks following birth and at three months post …
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Modelling Locally Changing Variance Structured Time Series Data By Using Breakpoints Bootstrap Filtering
… such processes deal with time series of sparse data. Studies in such cases focus in the analysis, construction and prediction in parametric models. Here, we assume several non-linear time series with additive noise components, and the model fitting is proposed in two stages. The first stage …
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Statistical Analysis of Longitudinal and Multivariate Discrete Data
… in medical, biological and epidemiological longitudinal studies. Modeling and simulating such variables is difficult because the correlations are restricted by the marginal means via Fréchet bounds in a complicated way. In this dissertation we will first discuss partially specified models …
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The impact of medication therapy management on polypharmacy in people living with HIV/AIDS
… services on polypharmacy in PLWHA. A secondary data analysis of a new MTM project by the CDC, UNTHSC, and Walgreens that involved the collaboration of pharmacists and clinicians to provide patient-centered care for HIV patients was done. The study involved 765 participants from 10 states in the …
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Analysis of Continuous Longitudinal Data with ARMA(1, 1) and Antedependence Correlation Structures
<p>Longitudinal or repeated measure data are common in biomedical and clinical trials. These data are often collected on individuals at scheduled times resulting in dependent responses. Inference methods for studying the behavior of responses over time as well as methods to study the association …
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Tropical Cyclone Hazards in Relation to Propagation Speed
… angles, and cyclone averaged winds. This analysis is focused on tropical cyclones spanning from 1950-2015 in the North Atlantic. We first confirm with other research that the temporal trends of intensity of tropical cyclones do not show a consistent temporal signal over the entire record. …
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Using Time Series Models for Defect Prediction in Software Release Planning
To produce a high-quality software release, sufficient time should be allowed for testing and fixing defects. Otherwise, there is a risk of slip in the development schedule and/or software quality. A time series model is used to predict the number of bugs created during development. The model …
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D-Vine Pair-Copula Models for Longitudinal Binary Data
<p>Dependent longitudinal binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. A popular method for analyzing such data is the multivariate probit (MP) model. The motivation for this dissertation stems from the fact that the MP model fails even the …
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Canonical Correlation Analysis for Longitudinal Data
<p>Data (multivariate data) on two sets of vectors commonly occur in applications. Statistical analysis of these data is usually done using a canonical correlation analysis (CCA). Occurrence of these data at multiple occasions or conditions leads to longitudinal multivariate data for a CCA. We …
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The role of psychological wellbeing in adolescence: Its relation to psychological symptoms/behaviors
… of identity. These aims were answered by a meta-analysis encompassing studies of symptoms/behaviors and psychological wellbeing (PWB) within adolescence (i.e., youth aged 10-17; 18 unique datasets, 5880 total participants, average mean age: 15.19 years). The second study extended this research by …
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