Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Differential expression (2)
- Propensity score (2)
- Bayesian (1)
- Biomarker (1)
- Causal inference (1)
-
- Clustered data (1)
- Confounding (1)
- Cross-validation (1)
- Cut-point Selection (1)
- Diagnostic accuracy test (1)
- Exponential tilting (1)
- Factor model (1)
- Fractional polynomial (1)
- Gene expression (1)
- Gene networks (1)
- Gene set analysis (1)
- Generalized Youden Index (1)
- Growth and yield modeling (1)
- Height-age relationship (1)
- Hurdle model (1)
- Imputation (1)
- Influential function (1)
- Informative cluster size (1)
- Informative within-cluster group size (1)
- Kullback-Leibler divergence (1)
- Linear methods (1)
- Loblolly pine plantations (1)
- Logistic regression (1)
- MC-SIMEX (1)
- Machine learning (1)
Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
Statistical Approaches Of Gene Set Analysis With Quantitative Trait Loci For High-Throughput Genomic Studies., Samarendra Das
Statistical Approaches Of Gene Set Analysis With Quantitative Trait Loci For High-Throughput Genomic Studies., Samarendra Das
Electronic Theses and Dissertations
Recently, gene set analysis has become the first choice for gaining insights into the underlying complex biology of diseases through high-throughput genomic studies, such as Microarrays, bulk RNA-Sequencing, single cell RNA-Sequencing, etc. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Further, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. Hence, a comprehensive overview of the available gene set analysis approaches used for different high-throughput genomic studies is provided. The analysis of gene sets is usually carried out based on …
Modified-Half-Normal Distribution And Different Methods To Estimate Average Treatment Effect., Jingchao Sun
Modified-Half-Normal Distribution And Different Methods To Estimate Average Treatment Effect., Jingchao Sun
Electronic Theses and Dissertations
This dissertation consists of three projects related to Modified-Half-Normal distribution and causal inference. In my first project, a new distribution called Modified-Half-Normal distribution was introduced. I explored a few of its distributional properties, the procedures for generating random samples based on Bayesian approaches, and the parameter estimation based on the method of moments. The second project deals with the problem of selection bias of average treatment effect (ATE) if we use the observational data. I combined the propensity score based inverse probability of treatment weighting (IPTW) method and the directed acyclic graph (DAG) to solve this problem. The third project …
Aspects Of Causal Inference., John A. Craycroft
Aspects Of Causal Inference., John A. Craycroft
Electronic Theses and Dissertations
Observational studies differ from experimental studies in that assignment of subjects to treatments is not randomized but rather occurs due to natural mechanisms, which are usually hidden from researchers. Yet objectives of the two studies are frequently the same: identify the causal – rather than merely associational – relationship between some treatment or exposure and an outcome. The statistical issues that arise in properly analyzing observational data for this goal are numerous and fascinating, and these issues are encompassed in the domain of causal inference. The research presented in this dissertation explores several distinct aspects of causal inference. This dissertation …
The Influence Of Environmental Variables On The Height Growth Of Loblolly Pine (Pinus Taeda) In The Western Gulf, Osakpamwan Edo-Iyasere
The Influence Of Environmental Variables On The Height Growth Of Loblolly Pine (Pinus Taeda) In The Western Gulf, Osakpamwan Edo-Iyasere
Electronic Theses and Dissertations
Understanding the effects of environmental factors on stand growth is important in optimizing forest management plans. This study investigated the effects of soil and climate factors on the height growth (site index) of loblolly pine (Pinus Taeda L.) using data collected from permanent plots established in intensively-managed plantations across East Texas and Western Louisiana. The Chapman-Richards model was selected as the base model to describe the height-age relationships and important soil and climate variables were incorporated into the models as model parameter coefficient adjustors. Our results showed that the most important factors for predicting site index were nitrogen …
Marginal Methods And Software For Clustered Data With Cluster- And Group-Size Informativeness., Mary Elizabeth Gregg
Marginal Methods And Software For Clustered Data With Cluster- And Group-Size Informativeness., Mary Elizabeth Gregg
Electronic Theses and Dissertations
Clustered data result when observations have some natural organizational association. In such data, cluster size is defined as the number of observations belonging to a cluster. A phenomenon termed informative cluster size (ICS) occurs when observation outcomes vary in a systematic way related to the cluster size. An additional form of informativeness, termed informative within-cluster group size (IWCGS), arises when the distribution of group-defining categorical covariates within clusters similarly carries information related to outcomes. Standard methods for the marginal analysis of clustered data can produce biased estimates and inference when data have informativeness. A reweighting methodology has been developed that …
Linear Methods For Regression With Small Sample Sizes Relative To The Number Of Variables., Rajesh Sikder
Linear Methods For Regression With Small Sample Sizes Relative To The Number Of Variables., Rajesh Sikder
Electronic Theses and Dissertations
In data sets where there are a small number of observations but a large number of variables observed for each observation, ordinary least squares estimation cannot be used for regression models. There are many alternative including stepwise regression, penalized methods such as ridge regression and the LASSO, and methods based on derived inputs such as principal components regression and partial least squares regression. In this thesis, these five methods are described. K-fold cross validation is also discussed as a way for determining regularization parameters for each method. The performance of these methods in estimation and prediction is also examined through …
Novel Bayesian Methodology For The Analysis Of Single-Cell Rna Sequencing Data., Michael Sekula
Novel Bayesian Methodology For The Analysis Of Single-Cell Rna Sequencing Data., Michael Sekula
Electronic Theses and Dissertations
With single-cell RNA sequencing (scRNA-seq) technology, researchers are able to gain a better understanding of health and disease through the analysis of gene expression data at the cellular-level; however, scRNA-seq data tend to have high proportions of zero values, increased cell-to-cell variability, and overdispersion due to abnormally large expression counts, which create new statistical problems that need to be addressed. This dissertation includes three research projects that propose Bayesian methodology suitable for scRNA-seq analysis. In the first project, a hurdle model for identifying differentially expressed genes across cell types in scRNA-seq data is presented. This model incorporates a correlated random …
Generalization Of Kullback-Leibler Divergence For Multi-Stage Diseases: Application To Diagnostic Test Accuracy And Optimal Cut-Points Selection Criterion, Chen Mo
Electronic Theses and Dissertations
The Kullback-Leibler divergence (KL), which captures the disparity between two distributions, has been considered as a measure for determining the diagnostic performance of an ordinal diagnostic test. This study applies KL and further generalizes it to comprehensively measure the diagnostic accuracy test for multi-stage (K > 2) diseases, named generalized total Kullback-Leibler divergence (GTKL). Also, GTKL is proposed as an optimal cut-points selection criterion for discriminating subjects among different disease stages. Moreover, the study investigates a variety of applications of GTKL on measuring the rule-in/out potentials in the single-stage and multi-stage levels. Intensive simulation studies are conducted to compare the performance …
Nonparametric Misclassification Simulation And Extrapolation Method And Its Application, Congjian Liu
Nonparametric Misclassification Simulation And Extrapolation Method And Its Application, Congjian Liu
Electronic Theses and Dissertations
The misclassification simulation extrapolation (MC-SIMEX) method proposed by Küchenho et al. is a general method of handling categorical data with measurement error. It consists of two steps, the simulation and extrapolation steps. In the simulation step, it simulates observations with varying degrees of measurement error. Then parameter estimators for varying degrees of measurement error are obtained based on these observations. In the extrapolation step, it uses a parametric extrapolation function to obtain the parameter estimators for data with no measurement error. However, as shown in many studies, the parameter estimators are still biased as a result of the parametric extrapolation …
Multiple Imputation Using Influential Exponential Tilting In Case Of Non-Ignorable Missing Data, Kavita Gohil
Multiple Imputation Using Influential Exponential Tilting In Case Of Non-Ignorable Missing Data, Kavita Gohil
Electronic Theses and Dissertations
Modern research strategies rely predominantly on three steps, data collection, data analysis, and inference. In research, if the data is not collected as designed, researchers may face challenges of having incomplete data, especially when it is non-ignorable. These situations affect the subsequent steps of evaluation and make them difficult to perform. Inference with incomplete data is a challenging task in data analysis and clinical trials when missing data related to the condition under the study. Moreover, results obtained from incomplete data are prone to biases. Parameter estimation with non-ignorable missing data is even more challenging to handle and extract useful …