Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Bayesian (2)
- Survival Analysis (2)
- Alternating Minimization (1)
- Bayesian framework (1)
- Brain network model (1)
-
- Breslow Estimator (1)
- Categorical Cluster Analysis (1)
- Cluster Analysis (1)
- Covariate space (1)
- Cox proportional hazards model (1)
- Dimensional data (1)
- Dual Path (1)
- Dynamic Prediction (1)
- Functional MRI (1)
- Fuzzy Ensemble-Based Clustering (1)
- Global Terrorism Database (1)
- Group fused lasso (1)
- LASSO (1)
- Laplace Regression (1)
- Leave-One-Covariate-Out (1)
- Longitudinal data (1)
- Louis's Method (1)
- Markov chain Monte Carlo (1)
- Measurement error (1)
- Metropolis Hastings algorithm (1)
- Mixed effect model (1)
- Monotone Splines (1)
- Multiplicative models (1)
- Multivariate Joint Model (1)
- Ordered Time-to-Event (1)
Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Estimation And Inference Under Model Uncertainty, Yizheng Wei
Estimation And Inference Under Model Uncertainty, Yizheng Wei
Theses and Dissertations
Chapter 1 of this dissertation proposes a consistent and locally efficient estimator to estimate the model parameters for a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where in many cases we have limited resources to design experiments and gather data in long-term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial if we could have an estimator …
Categorical And Fuzzy Ensemble-Based Algorithms For Cluster Analysis, Bridget Nicole Manning
Categorical And Fuzzy Ensemble-Based Algorithms For Cluster Analysis, Bridget Nicole Manning
Theses and Dissertations
This dissertation focuses on improving multivariate methods of cluster analysis. In Chapter 3 we discuss methods relevant to the categorical clustering of tertiary data while Chapter 4 considers the clustering of quantitative data using ensemble algorithms. Lastly, in Chapter 5, future research plans are discussed to investigate the clustering of spatial binary data.
Cluster analysis is an unsupervised methodology whose results may be influenced by the types of variables recorded on observations. When dealing with the clustering of categorical data, solutions produced may not accurately reflect the structure of the process that generated them. Increased variability within the latent structure …
Incorporation And Measurement Of Uncertainty In Clustered And Spatial Data, Yuan Hong
Incorporation And Measurement Of Uncertainty In Clustered And Spatial Data, Yuan Hong
Theses and Dissertations
Analyzing population representative datasets for local estimation and predictions over time is important for monitoring related public health issues, however, there are many statistical challenges associated with such analyses. Mixed effect models are one of the common options which can incorporate time and spatial effect in the model and related inference is well established.
In the first part of this dissertation, to estimate area-level prevalence using individuallevel data, small area estimation (SAE) with post-stratified mixed effect models were used where sampling weights were also incorporated into it. However, if poststratification which requires more computation effort can improve estimation accuracy is …
High-Dimensional Inference Based On The Leave-One-Covariate-Out Regularization Path, Xiangyang Cao
High-Dimensional Inference Based On The Leave-One-Covariate-Out Regularization Path, Xiangyang Cao
Theses and Dissertations
The increasingly rapid emergence of high dimensional data, where the number of variables p may be larger than the sample size n, has necessitated the development of new statistical methodologies. LASSO and variants of LASSO are proposed and have been the most popular estimators for the high dimensional regression models. However, not much work has focused on analyzing and summarizing the information contained in the entire solution path of the LASSO. This dissertation consists of three research projects that propose and extend the Leave-One-Covariate-Out(LOCO) solution path statistic to regression and graphical models.
In the first chapter, we propose a new …
The Practical Advantages And Disadvantages Of Laplace Regression As An Alternative To Cox Proportional Hazards Model: A Comparison Via Simulation, Sydney Smith
Theses and Dissertations
The Cox proportional hazards model is the most common regression technique for survival analysis. However, the proportional hazards assumption restricts it’s use to a limited group of multiplicative models. Laplace regression is a flexible quantile regression technique for censored observations that is appropriate in a wider variety of applications as compared to the Cox proportional hazards model. Instead of estimating a hazard ratio, Laplace regression which is free from a proportionality assumption, can be used to estimate many adjusted percentiles of survival time allowing for a more complete description of the association of interest. This paper compares the performance of …
Semiparametric Regression Analysis Of Survival Data And Panel Count Data, Lu Wang
Semiparametric Regression Analysis Of Survival Data And Panel Count Data, Lu Wang
Theses and Dissertations
Both censored survival data and panel count data arise commonly in real-life studies in many fields such as epidemiology, social science, and medical research. In these studies, subjects are usually examined multiple times at periodical or irregular follow-up examinations. Censored data are studied when the exact failure times of the events are of interest but not all of these exact times are directly observed. Some of the failure times of event of interest are only known to fall within some intervals formed by the observation times. Panel count data are under investigation when the exact times of the recurrent events …
Network-Based Statistical Analysis Of Functional Magnetic Resonance Imaging Data From Aphasia Patients, Xingpei Zhao
Network-Based Statistical Analysis Of Functional Magnetic Resonance Imaging Data From Aphasia Patients, Xingpei Zhao
Theses and Dissertations
Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that provides insight into brain function and activity. Network models of fMRI signals can reveal functional connectivity related to certain brain disorders, such as post-stroke aphasia. This thesis aims to identify the functional connections that distinguish anomic and Broca’s aphasia by comparing the resting-state fMRI from the patients with these two types of aphasia. The network-based statistic (NBS) approach is used to detect such connections. After the analytic pipeline is applied to the fMRI data, the NBS approach identifies a distinct subnetwork between the two types of aphasia, which involves the …
Bayesian Zero-Inflated Model For Ordinal Data, Huizhong Yang
Bayesian Zero-Inflated Model For Ordinal Data, Huizhong Yang
Theses and Dissertations
Datasets with a relatively large number of zeros is commonly seen in medical applications. Although models like Zero-inflated Poisson (ZIP) model are proposed for counts data, there is still some issues with ordinal data which have excess zeros. In this paper, we developed a Bayesian approach to accommodate the excess zero in ordinal data. Intellectual disability (ID), also known as mental retardation (MR), is a disability characterized by below-average intelligence or mental ability and a lack of the learning necessary skills for daily life. A person with intellectual disability has intellectual functioning and adaptive behaviors limitations. Intellectual disability is a …
Studies Of Group Fused Lasso And Probit Model For Right-Censored Data, Tuan Quoc Do
Studies Of Group Fused Lasso And Probit Model For Right-Censored Data, Tuan Quoc Do
Theses and Dissertations
This document is composed of three main chapters. In the first chapter, we study the mixture of experts, a powerful machine learning model in which each expert handles a different region of the covariate space. However, it is crucial to choose an appropriate number of experts to avoid overfitting or underfitting. A group fused lasso (GFL) term is added to the model with the goal of making the coefficients of the experts and the gating network closer together. An algorithm to optimize the problem is also developed using block-wise coordinate descent in the dual counterpart. Numerical results on simulated and …
Flexible Regression Models For Survival Data, Ennan Gu
Flexible Regression Models For Survival Data, Ennan Gu
Theses and Dissertations
Survival analysis is a branch of statistics to analyze the time-to-event data or survival data. One important feature of survival data is censoring, which means that not all the subjects’ survival time are observed directly. Among all the survival data, right-censored data are the most common type and consist of some exactly observed survival times and some right-censored observations. In this dissertation, we focus on studying flexible regression models for complicated right-censored survival data when the classical proportional hazards (PH) assumption is not satisfied. Flexible semiparametric regression models can largely avoid misspecification of parametric distributions and thus provide more modeling …
Bayesian Analysis Of Binary Diagnostic Tests And Panel Count Data, Chunling Wang
Bayesian Analysis Of Binary Diagnostic Tests And Panel Count Data, Chunling Wang
Theses and Dissertations
This dissertation mainly explores several challenging topics that arise in diagnostic tests and panel count data in the Bayesian framework. Binary diagnostic tests, particularly multiple diagnostic tests with repeated measures and diagnostic procedures with a large number of raters, are studied. For panel count data, most traditional methods only handle panel count data for a single type of recurrent event. In this dissertation, we primarily focus on the case with multiple types of recurrent events.
In Chapter 1, an introduction to the binary diagnostic tests data and panel count data is presented and related literature works are briefly reviewed. To …
Multivariate Joint Models And Dynamic Predictions, Md Akhtar Hossain
Multivariate Joint Models And Dynamic Predictions, Md Akhtar Hossain
Theses and Dissertations
The joint modeling of longitudinal and time-to-event data is an active area of statistical research that has received a lot of attention. The standard joint models, referred to as univariate joint models, allow simultaneous modeling of a single longitudinal outcome and a single time-to-event under an assumption of independent censoring. The majority of the joint modeling research in the last two decades has focused on extending and improving the univariate joint models. While many of the practical applications involve data on multivariate longitudinal outcomes and multiple timeto- events possibly informatively censored by some other terminal time-to-event, the developments of joint …