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 32 for “"Longitudinal Data Analysis and Time Series"”.
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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
<p>This 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. …
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Modelling Locally Changing Variance Structured Time Series Data By Using Breakpoints Bootstrap Filtering
… applications in many areas such as oceanography and engineering. Special classes of 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 …
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Statistical Analysis of Longitudinal and Multivariate Discrete Data
<p>Correlated multivariate Poisson and binary variables occur naturally 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 …
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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
<p>As the population and infrastructure along the US East Coast increase, it becomes increasingly important to study the characteristics of tropical cyclones that can impact the coast. A recent study shows that the propagation speed of tropical cyclones has slowed over the past 60 years, which can …
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Using Time Series Models for Defect Prediction in Software Release Planning
… 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 depends on …
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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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Dynamic Prediction of Disease Progression With Longitudinal Data
… clinical research, especially when forecasting time-to-event outcomes based on evolving longitudinal data. This process often leverages the integration of longitudinal and time-to-event data through joint modeling, a prevalent technique. Alongside joint modeling, landmark modeling stands as …
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Estimation of Parameters in Replicated Time Series Regression Models
<p>The time series regression model was widely studied in the literature by several authors. However, statistical analysis of replicated time series regression models has received little attention. In this thesis, we study the application of quasi-least squares, a relatively new method, to estimate …
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Methods for the Analysis of Developmental Respiration Patterns.
… crassipalpis</em> Macquart from the biological and instrumental points of view and adapts mathematical and statistical tools in order to analyze the data gathered. The biological motivation and current state of research is given as well as instrumental considerations and problems in the …
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Anticoagulant Treatment Effects Assessment in Antiphospholipid Syndrome Patients Using Targeted Learning and the Oracle Health EHR Data
… by an increased risk of both arterial and venous blood clots, leading to significant health complications and even death. While warfarin has traditionally been the go-to anticoagulant for most patients with APS, many clinicians are now turning to direct oral anticoagulants (DOACs). …
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Copula-Based Zero-Inflated Count Time Series Models
<p>Count time series data are observed in several applied disciplines such as in environmental science, biostatistics, economics, public health, and finance. In some cases, a specific count, say zero, may occur more often than usual. Additionally, serial dependence might be found among these counts …
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Comparison of Time Series and Functional Data Analysis for the Study of Seasonality.
<p>Classical time series analysis has well known methods for the study of seasonality. A more recent method of functional data analysis has proposed phase-plane plots for the representation of each year of a time series. However, the study of seasonality within functional data analysis has not been …
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Machine Learning and Geostatistical Approaches for Discovery of Weather and Climate Events Related to El Niño Phenomena
<p>El Nino and La Nina are worldwide environmental phenomena brought about by repetitive changes in the water temperature of the Pacific Ocean. Even though the El-Nino impact focuses on a smaller area in the Pacific Ocean near the Equator, these developments have global repercussions, where …
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Statistical Modeling of Longitudinal Medical Cost Data
… cost is critical in health economics research and policy making. An indispensable step is to estimate cost trajectories from an incident cohort of cancer patients using longitudinal medical cost data, accounting for terminal events such as death, and right censoring due to loss of follow-up. …
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A Novel Correction for the Multivariate Ljung-Box Test
… approach for the more complex, multidimensional time series scenarios. We use large sample simulation data for a range of values of sample sizes, lags, and number of time series to obtain an empirical estimation of the correct rejection regions for the particular combination of values of these …
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