Browsing Statistics by Title
Now showing items 10-29 of 121
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Bayesian Lasso Models – With Application to Sports Data
(North Dakota State University, 2018)Several statistical models were proposed by researchers to fulfill the objective of correctly predicting the winners of sports game, for example, the generalized linear model (Magel & Unruh, 2013) and the probability ... -
Bayesian Sparse Factor Analysis of High Dimensional Gene Expression Data
(North Dakota State University, 2019)This work closely studied fundamental techniques of Bayesian sparse Factor Analysis model - constrained Least Square regression, Bayesian Lasso regression, and some popular sparsity-inducing priors. In Appendix A, we ... -
Boundary Estimation
(North Dakota State University, 2015)The existing statistical methods do not provide a satisfactory solution to determining the spatial pattern in spatially referenced data, which is often required by research in many areas including geology, agriculture, ... -
Bracketing NCAA Men's Division I Basketball Tournament
(North Dakota State University, 2013)This paper presents a new bracketing method for all 63 games in the NCAA Division 1 basketball tournament. This method, based on the logistic conditional probability models, is self-consistent in terms of constructing ... -
Bracketing the NCAA Women's Basketball Tournament
(North Dakota State University, 2014)This paper presents a bracketing method for all the 63 games in NCAA Division I Women's basketball tournament. Least squares models and logistic regression models for Round 1, Round 2 and Rounds 3-6 were developed, to ... -
Clustering Algorithm Comparison for Ellipsoidal Data
(North Dakota State University, 2015)The main objective of cluster analysis is the statistical technique of identifying data points and assigning them into meaningful clusters. The purpose of this paper is to compare different types of clustering algorithms ... -
Comparative Analysis of Traditional and Modified DECODE Method in Small Sample Gene Expression Experiments
(North Dakota State University, 2018)Background: The DECODE method integrates differential co-expression and differential expression analysis methods to better understand biological functions of genes and their associations with disease. The DECODE method ... -
Comparative Classification of Prostate Cancer Data using the Support Vector Machine, Random Forest, Dualks and k-Nearest Neighbours
(North Dakota State University, 2015)This paper compares four classifications tools, Support Vector Machine (SVM), Random Forest (RF), DualKS and the k-Nearest Neighbors (kNN) that are based on different statistical learning theories. The dataset used is a ... -
A Comparative Multiple Simulation Study for Parametric and Nonparametric Methods in the Identification of Differentially Expressed Genes
(North Dakota State University, 2021)RNA-seq data simulated from a negative binomial distribution, sampled without replacement, or modified from read counts were analyzed to compare differential gene expression analysis methods in terms of false discovery ... -
Comparing Accuracies of Spatial Interpolation Methods on 1-Minute Ground Magnetometer Readings
(North Dakota State University, 2017)Geomagnetic disturbances caused by external solar events can create geomagnetically induced currents (GIC) throughout conducting networks of Earth’s surface. GIC can cause disruption that scales from minor to catastrophic. ... -
Comparing Dunnett's Test with the False Discovery Rate Method: A Simulation Study
(North Dakota State University, 2013)Recently, the idea of multiple comparisons has been criticized because of its lack of power in datasets with a large number of treatments. Many family-wise error corrections are far too restrictive when large quantities ... -
Comparing Performance of ANOVA to Poisson and Negative Binomial Regression When Applied to Count Data
(North Dakota State University, 2020)Analysis of Variance (ANOVA) is the easiest and most widely used model nowadays in statistics. ANOVA however requires a set of assumptions for the model to be a valid choice and for the inferences to be accurate. Among ... -
Comparing Prediction Accuracies of Cancer Survival Using Machine Learning Techniques and Statistical Methods in Combination with Data Reduction Methods
(North Dakota State University, 2022)This comparative study of five-year survival prediction for breast, lung, colon, and leukemia cancers using a large SEER dataset along with 10-fold cross-validation provided us with an insight into the relative prediction ... -
Comparing Prediction Methods of Wheat Grain Quality With the Area Under the Receiver Operating Characteristic Curves
(North Dakota State University, 2021)A widely used breeding method is genomic selection, which uses genome-wide marker coverage to predict genotypic values for quantitative traits. Genomic selection combines molecular and phenotypic data in a training population ... -
Comparing Several Modeling Methods on NCAA March Madness.
(North Dakota State University, 2015)This year (2015), according to the AGA’s (American Gaming Association) research, nearly about 40 million people filled out about 70 million March Madness brackets (Moyer, 2015). Their objective is to correctly predict the ... -
Comparing Tests for a Mixed Design with Block Effect
(North Dakota State University, 2009)Tests Comb and Comb II are used to test the equality of means in a mixed design which is a combination of randomized complete block design and completely randomized design. The powers of Comb and Comb II for a mixed ... -
Comparing Total Hip Replacement Drug Treatments for Cost and Length of Stay
(North Dakota State University, 2015)The objective of this study is to identify the potential effect anticoagulants, spinal blocks, and antifibrinolytics have on overall cost, length of stay, and re-admission rates for total hip replacement patients. We use ... -
Comparison of Classification Rates among Logistic Regression, Neural Network and Support Vector Machines in the Presence of Missing Data
(North Dakota State University, 2014)Statistical models such as Logistic Regression (LR), Neural Network (NN) and Support Vector Machines (SVM) often use datasets with missing values while making inferences regarding the population. When inferences are made ... -
A Comparison of False Discovery Rate Method and Dunnett's Test for a Large Number of Treatments
(North Dakota State University, 2015)It has become quite common nowadays to perform multiple tests simultaneously in order to detect differences of a certain trait among groups. This often leads to an inflated probability of at least one Type I Error, a ... -
A Comparison of Filtering and Normalization Methods in the Statistical Analysis of Gene Expression Experiments
(North Dakota State University, 2020)Both microarray and RNA-seq technologies are powerful tools which are commonly used in differential expression (DE) analysis. Gene expression levels are compared across treatment groups to determine which genes are ...