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Full-Text Articles in Physical Sciences and Mathematics
Bayesian Semiparametric Methods For Analyzing Panel Count Data, Jianhong Wang
Bayesian Semiparametric Methods For Analyzing Panel Count Data, Jianhong Wang
Theses and Dissertations
Panel count data commonly arise in epidemiological, social science, medical studies, in which subjects have repeated measurements on the recurrent events of interest at different observation times. Since the subjects are not under continuous monitoring, the exact times of those recurrent events are not observed but the counts of such events within the adjacent observation times are known. Panel count data can be considered as a special type of longitudinal data with a count response variable in the literature. Compared to the frequentist literature, very limited Bayesian approaches have been developed to analyze panel count data. In this dissertation, several …
Discovery Of Community Structures In Static And Dynamic Networks, Shiwen Shen
Discovery Of Community Structures In Static And Dynamic Networks, Shiwen Shen
Theses and Dissertations
With the development of computer technology, researchers are able to observe and collect enormous amount of data, where the independent and identical distributed assumption is violated. For example, in sociology, individuals in an organization interact with each other to change the underlying social structure; in biology, understanding the gene-gene interaction helps researchers to detect potential diseases; in politics, voters are mutually influenced before the election via private/public speeches and parades, which might ultimately change the election results. It is crucial to study how individuals interact with each other from the data, which would lead to tremendous contributions to the society. …
Classification Of High-Dimensional Data Based On Multiple Testing Methods, Chong Ma
Classification Of High-Dimensional Data Based On Multiple Testing Methods, Chong Ma
Theses and Dissertations
Supervised and unsupervised classification are common topics in machine learning in both scientific and industrial fields, which usually involve three tasks: prediction, exploration, and explanation. False discovery rate (FDR) theory has a close connection to classical classification theory, which must be employed in a sophisticated way to achieve good performance in various contexts. The study aims to explore novel supervised classifiers and unsupervised classification approaches for functional data and high-dimensional data in genome study by using FDR, respectively. One work develops a novel classifier for functional data by casting the classification problem into a multiple testing task, which involves using …
Goodness Of Fit Via Residual Plots In Item Response Theory, Bryonna Bowen
Goodness Of Fit Via Residual Plots In Item Response Theory, Bryonna Bowen
Theses and Dissertations
Goodness-of-fit criteria developed for the evaluation of item response functions have been examined by many scholars using different theories and criteria. A number of potential graphical analysis approaches, such as residual plots, have been described in literature, but have received little attention from researchers. While many tests of goodness-of-fit are available, those that incorporate the analysis of residuals may be most useful. The unmistakable presence of a pattern in the residual plot for the logistic model item response functions even when we know the model fits raises a red flag up and calls for greater analysis. This study explores different …
Semiparametric Statistical Estimation And Inference With Latent Information, Qianqian Wang
Semiparametric Statistical Estimation And Inference With Latent Information, Qianqian Wang
Theses and Dissertations
In Chapter 1, we predicted disease risk by transformation models in the presence of missing subgroup identifiers. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific distributions. Taking into account the uncertain distribution of the group membership and the covariates, we model the relation between the disease onset time and the covariates through transformation models in each sub-population, and develop a nonparametric maximum likelihood based estimation implemented through EM algorithm along with its inference procedure. We further propose methods to …