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.
Results
Showing 1 to 20 of 414 for “"longitudinal data"”.
-
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 …
-
Semiparametric analysis of multivariate longitudinal data
… OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] Longitudinal studies are conducted widely in fields such as agriculture and life sciences, business and industry, demography and other social sciences, medicine and public health. In longitudinal studies, individuals are measured repeatedly over …
-
Bayesian Analysis of Discrete Longitudinal Data
… this model recognizes the discrete nature of the data, as well as its time dependency. Bayesian analysis is used to make inference on each individual profile, as well as on a group profile for each treatment group.
-
Dynamic Prediction of Disease Progression With Longitudinal Data
… 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 another key approach in the realm of …
-
Canonical Correlation and Correspondence Analysis of Longitudinal Data
… relationships between two sets of repeatedly or longitudinally observed data. When the two vectors are quantitative, we use a block Kronecker product matrix to model dependency of the variables over time. We then apply canonical correlation analysis on this matrix to obtain canonical correlations …
-
Modeling longitudinal data with interval censored anchoring events
In many longitudinal studies, the time scales upon which we assess the primary outcomes are anchored by pre-specified events. However, these anchoring events are often not observable and they are randomly distributed with unknown distribution. Without direct observations of the anchoring events, …
-
Modeling the Progression of Discrete Paired Longitudinal Data.
… a methodology for which to model discrete paired longitudinal data. Through the use of transition matrices and maximum likelihood estimation techniques by means of software, we develop a way to model the progression of such data. We provide an example by applying this method to the Wisconsin …
-
A Comparison for Longitudinal Data Missing Due to Truncation
Many longitudinal clinical studies suffer from patient dropout. Often the dropout is nonignorable and the missing mechanism needs to be incorporated in the analysis. The methods handling missing data make various assumptions about the missing mechanism, and their utility in practice depends on …
-
Analysis of Multivariate Longitudinal Data Using Structural Equation Modeling
… interests in modeling and analyzing multivariate longitudinal data. This dissertation research has four chapters that focus on diffferent topics of structural equation modeling. The first chapter gives a brief introduction to structural equation modeling. The second chapter analyzed factors …
-
Canonical Variate Analysis and Related Methods with Longitudinal Data
… for analyzing group structure in multivariate data. It is mathematically equivalent to a one-way multivariate analysis of variance and often goes by the name of canonical discriminant analysis. Change over time is a central feature of many phenomena of interest to researchers. This dissertation …
-
JOINT MODELING OF MULTIVARIATE LONGITUDINAL DATA AND COMPETING RISKS DATA
… it is common to observe one or more sequences of longitudinal measurements, as well as one or more time to event outcomes. A competing risks situation arises when the probability of occurrence of one event isaltered/hindered by another time to event. A classical example is different causes of …
-
Modelling longitudinal data on respiratory infections to inform health policy
… for the challenges posed by surveillance data. The observational nature of the available information, affected by confounding, makes causal statements difficult. Improvements to routinely employed methodologies are proposed, employing phenomenological models to estimate a counterfactual, …
-
INVESTIGATION OF RESILIENCE AND ITS GENETICSTHROUGH THE USE OF LONGITUDINAL DATA
… Northern Italy, we had access to a comprehensive dataset of daily milk records and genotype information, which formed the basis for the analysis. Over the past twenty years, resilience assessment in livestock has become feasible thanks to new technologies that enable the collection of …
-
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 …
-
Analysis of Longitudinal Data With Missing Responses: A Study of Pain Control Cost
… or reduce the cost of pain control. The first data studied in this research is longitudinal data with cost values completely missing. We fill in the daily cost for minority observations based on the information provided by the price data, and then impute the daily cost for the remaining …
-
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 …
Page 1 of 21