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Now showing items 31-40 of 121
Modeling Loss Severity With Lognormal Mixtures
(North Dakota State University, 2015)
Property and Casualty insurance companies set premium rates by evaluating both loss fre-
quency and loss severity data. Insurance companies often model severity using a well-known single
distribution such as Lognormal ...
Investigating Statistical vs. Practical Significance of the Kolmogorov-Smirnov Two-Sample Test Using Power Simulations and Resampling Procedures
(North Dakota State University, 2018)
This research examines the power of the Kolmogorov-Smirnov two-sample test. The motivation for this research is a large data set containing soil salinity values. One problem encountered was that the power of the ...
Empirical Study of Two Hypothesis Test Methods for Community Structure in Networks
(North Dakota State University, 2019)
Many real-world network data can be formulated as graphs, where a binary relation exists between nodes. One of the fundamental problems in network data analysis is community detection, clustering the nodes into different ...
On K-Means Clustering Using Mahalanobis Distance
(North Dakota State University, 2012)
A problem that arises quite frequently in statistics is that of identifying groups, or clusters, of data within a population or sample. The most widely used procedure to identify clusters in a set of observations is known ...
Robust D-Optimal Design for Response Functions with a Downturn
(North Dakota State University, 2013)
Researchers studying dose-response relationships must allocate limited resources to design points in order to maximize the information gained from the study. D-optimal design is a well-described design that works efficiently ...
Proposed Nonparametric Tests for the Simple Tree Alternative in a Mixed Design
(North Dakota State University, 2014)
For the general alternative, many test statistics exist for the dependent and independent
variables. However, no documented test statistics exist for simple tree alternative for the dependent
variables, independent ...
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 ...
Predicting Outcomes of NBA Basketball Games
(North Dakota State University, 2016)
A stratified random sample of 144 NBA basketball games was taken over a three-year period, between 2008 and 2011. Models were developed to predict point spread and to estimate the probability of a specific team winning ...
Empirical Comparison of Statistical Tests of Dense Subgraph in Network Data
(North Dakota State University, 2020)
Network analysis is useful in modeling the structures of different phenomena. A fundamental question in the analysis of network data is whether a network contains community structure. One type of community structure of ...
Prediction of the World Cup Soccer Winner: Using Two Statistical Methods
(North Dakota State University, 2016)
Soccer is considered the most popular sport on earth and applying statistical models to analyze small soccer data has been of a keen interest to modern researchers. Statistical modeling of soccer data also provides guidance ...