On Modeling Quantities For Insurer Solvency Against Catastrophe Under Some Markovian Assumptions,
2018
Missouri University of Science and Technology
On Modeling Quantities For Insurer Solvency Against Catastrophe Under Some Markovian Assumptions, Daniel Jefferson Geiger
Doctoral Dissertations
"Insurance companies sometimes face catastrophic losses, yet they must remain solvent enough to meet the legal obligation of covering all claims. Catastrophes can result in large damages to the policyholders, causing the arrival of numerous claims to insurance companies at once. Furthermore, the severity of an event could impact the time until the next occurrence. An insurer needs certain levels of startup capital to meet all claims, and then must have adequate reserves on a continual basis, even more so when catastrophes occur. This work examines two facets of these matters: for an infinite time horizon, we extend and develop …
Development Of Biclustering Techniques For Gene Expression Data Modeling And Mining,
2018
South Dakota State University
Development Of Biclustering Techniques For Gene Expression Data Modeling And Mining, Juan Xie
Electronic Theses and Dissertations
The next-generation sequencing technologies can generate large-scale biological data with higher resolution, better accuracy, and lower technical variation than the arraybased counterparts. RNA sequencing (RNA-Seq) can generate genome-scale gene expression data in biological samples at a given moment, facilitating a better understanding of cell functions at genetic and cellular levels. The abundance of gene expression datasets provides an opportunity to identify genes with similar expression patterns across multiple conditions, i.e., co-expression gene modules (CEMs). Genomescale identification of CEMs can be modeled and solved by biclustering, a twodimensional data mining technique that allows clustering of rows and columns in a gene …
Rock Paper Scissors And Evolutionary Game Theory,
2018
University of Nevada, Las Vegas
Rock Paper Scissors And Evolutionary Game Theory, Christian Cordova, Rudolf Jovero, Evan Thomas
Math 365 Class Projects
In Rock Paper Scissors (RPS), three different "species" compete, but no single species has a dominating strategy. In evolutionary game theory, replicator equations model population densities over time. When a mutation is introduced, they are called "replicator-mutator" equations. Using the replicator-mutator equation in [1] we have shown how population density of three species change.
Type I General Exponential Class Of Distributions,
2018
Marquette University
Type I General Exponential Class Of Distributions, Gholamhossein G. Hamedani, Haitham M. Yousof, Mahdi Rasekhi, Morad Alizadeh, Seyed Morteza Najibi
Mathematics, Statistics and Computer Science Faculty Research and Publications
We introduce a new family of continuous distributions and study the mathematical properties of the new family. Some useful characterizations based on the ratio of two truncated moments and hazard function are also presented. We estimate the model parameters by the maximum likelihood method and assess its performance based on biases and mean squared errors in a simulation study framework.
Prediction Intervals For Functional Data,
2018
Montclair State University
Prediction Intervals For Functional Data, Nicholas Rios
Theses, Dissertations and Culminating Projects
The prediction of functional data samples has been the focus of several functional data analysis endeavors. This work describes the use of dynamic function-on-function regression for dynamic prediction of the future trajectory as well as the construction of dynamic prediction intervals for functional data. The overall goals of this thesis are to assess the efficacy of Dynamic Penalized Function-on-Function Regression (DPFFR) and to compare DPFFR prediction intervals with those of other dynamic prediction methods. To make these comparisons, metrics are used that measure prediction error, prediction interval width, and prediction interval coverage. Simulations and applications to financial stock data from …
Spatio-Temporal Frequency Separation With Application Of Kolmogorov-Zurbenko Filters To The Multivariate Analysis Of Melanoma Prevalence,
2018
University at Albany, State University of New York
Spatio-Temporal Frequency Separation With Application Of Kolmogorov-Zurbenko Filters To The Multivariate Analysis Of Melanoma Prevalence, Edward Valachovic
Legacy Theses & Dissertations (2009 - 2024)
Time Series Analysis is the observation of variables recorded across time. Observations are visualized and analysis often performed in the native time domain. It is common for a time series to be the dependent variable of more than one factor. Several factors can have concurrent and combined effects. The time domain presents an obstacle due to constructive and destructive interference of factors at each time point. Unless effects are clearly pronounced and separable, the entanglement of factors along with the presence and intensity of random variation can obscure true relationships.
Stress-Strength Estimation And Its Applications In Clinical Trials,
2018
University at Albany, State University of New York
Stress-Strength Estimation And Its Applications In Clinical Trials, Dinesh Kumar
Legacy Theses & Dissertations (2009 - 2024)
Stress Strength model P(X
Race, Ethnicity, And The Great Recession : A National Evaluation Of Mortgages And Subprime Lending, 2004-2010,
2018
University at Albany, State University of New York
Race, Ethnicity, And The Great Recession : A National Evaluation Of Mortgages And Subprime Lending, 2004-2010, Meghan M. O'Neil
Legacy Theses & Dissertations (2009 - 2024)
The dissertation analyzes multilevel models to predict mortgage origination and the allocation of subprime credit pre-and-post Great Recession. With representative samples from two full years of mortgage applications filed in the top 100 U.S. metropolitan areas, the dissertation uncovers evidence of persistent disparities by race and neighborhood minority concentration despite controls for socioeconomic, demographic, assimilation and housing variables. Mortgage outcomes varied by applicant race, neighborhood racial composition and neighborhood racial change. Findings suggest evidence of Fair Housing Act violations and disparate impacts towards minority homebuyers and minority neighborhoods. Results lend support for spatial assimilation theories in explaining much of the …
Non-Stationary Counts With Mixture Distributions,
2018
University at Albany, State University of New York
Non-Stationary Counts With Mixture Distributions, Ziqiang Lin
Legacy Theses & Dissertations (2009 - 2024)
We study a new non--stationary mixture Pengram and thinning model for time series of counts that include the effect of covariate variables on the outcome variable. Properties of the model and performance are discussed. It has a simpler likelihood function than the non--stationary INAR(1) model and therefore MLE estimators for the model's parameters are easier to find. Therefore the model offers an alternative to non--stationary INAR(1).
Accounting For Spatial Autocorrelation In Modeling The Distribution Of Water Quality Variables,
2018
University of Kentucky
Accounting For Spatial Autocorrelation In Modeling The Distribution Of Water Quality Variables, Lorrayne Miralha
Theses and Dissertations--Geography
Several studies in hydrology have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this thesis, I hypothesized that SAC, inherently possessed by a response variable, influences spatial modeling outcomes. I selected ten watersheds in the USA and analyzed them to determine whether water quality variables with higher Moran’s I values undergo greater increases in the coefficient of determination (R²) and greater decreases in residual SAC (rSAC) after spatial modeling. I compared non-spatial ordinary …
Improved Methods And Selecting Classification Types For Time-Dependent Covariates In The Marginal Analysis Of Longitudinal Data,
2018
University of Kentucky
Improved Methods And Selecting Classification Types For Time-Dependent Covariates In The Marginal Analysis Of Longitudinal Data, I-Chen Chen
Theses and Dissertations--Epidemiology and Biostatistics
Generalized estimating equations (GEE) are popularly utilized for the marginal analysis of longitudinal data. In order to obtain consistent regression parameter estimates, these estimating equations must be unbiased. However, when certain types of time-dependent covariates are presented, these equations can be biased unless an independence working correlation structure is employed. Moreover, in this case regression parameter estimation can be very inefficient because not all valid moment conditions are incorporated within the corresponding estimating equations. Therefore, approaches using the generalized method of moments or quadratic inference functions have been proposed for utilizing all valid moment conditions. However, we have found that …
An Investigation Of Atomic Structures Derived From X-Ray Crystallography And Cryo-Electron Microscopy Using Distal Blocks Of Side-Chains,
2018
Old Dominion University
An Investigation Of Atomic Structures Derived From X-Ray Crystallography And Cryo-Electron Microscopy Using Distal Blocks Of Side-Chains, Lin Chen, Jing He, Salim Sazzed, Rayshawn Walker
Computer Science Faculty Publications
Cryo-electron microscopy (cryo-EM) is a structure determination method for large molecular complexes. As more and more atomic structures are determined using this technique, it is becoming possible to perform statistical characterization of side-chain conformations. Two data sets were involved to characterize block lengths for each of the 18 types of amino acids. One set contains 9131 structures resolved using X-ray crystallography from density maps with better than or equal to 1.5 Å resolutions, and the other contains 237 protein structures derived from cryo-EM density maps with 2-4 Å resolutions. The results show that the normalized probability density function of block …
Statistical Algorithms And Bioinformatics Tools Development For Computational Analysis Of High-Throughput Transcriptomic Data,
2018
South Dakota State University
Statistical Algorithms And Bioinformatics Tools Development For Computational Analysis Of High-Throughput Transcriptomic Data, Adam Mcdermaid
Electronic Theses and Dissertations
Next-Generation Sequencing technologies allow for a substantial increase in the amount of data available for various biological studies. In order to effectively and efficiently analyze this data, computational approaches combining mathematics, statistics, computer science, and biology are implemented. Even with the substantial efforts devoted to development of these approaches, numerous issues and pitfalls remain. One of these issues is mapping uncertainty, in which read alignment results are biased due to the inherent difficulties associated with accurately aligning RNA-Sequencing reads. GeneQC is an alignment quality control tool that provides insight into the severity of mapping uncertainty in each annotated gene from …
Variable Selection Techniques For Clustering On The Unit Hypersphere,
2018
South Dakota State University
Variable Selection Techniques For Clustering On The Unit Hypersphere, Damon Bayer
Electronic Theses and Dissertations
Mixtures of von Mises-Fisher distributions have been shown to be an effective model for clustering data on a unit hypersphere, but variable selection for these models remains an important and challenging problem. In this paper, we derive two variants of the expectation-maximization framework, which are each used to identify a specific type of irrelevant variables for these models. The first type are noise variables, which are not useful for separating any pairs of clusters. The second type are redundant variables, which may be useful for separating pairs of clusters, but do not enable any additional separation beyond the separability provided …
Goodness Of Fit Via Residual Plots In Item Response Theory,
2018
University of South Carolina
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,
2018
University of South Carolina
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 …
Bayesian Semiparametric Methods For Analyzing Panel Count Data,
2018
University of South Carolina
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 …
The South Carolina Safety Belt Study: Large-Scale Location Sampling,
2018
University of South Carolina
The South Carolina Safety Belt Study: Large-Scale Location Sampling, Stephanie Jones
Theses and Dissertations
The South Carolina Safety Belt Study is a statewide survey completed yearly to assess the prevalence of safety belt usage on of South Carolina roads through observations from different locations across the state. Every five years the sites for observation are resampled. This thesis breaks down the most recent sampling done for the years of 2018 through 2022. Both the methodology of large scale location sampling and the mathematical idea behind the strategy employed are covered. Further, three different software packages were utilized: R, SAS, and ArcGIS. The steps that were taken and the written function code run for each …
Comparison Of The Performance Of Simple Linear Regression And Quantile Regression With Non-Normal Data: A Simulation Study,
2018
University of South Carolina
Comparison Of The Performance Of Simple Linear Regression And Quantile Regression With Non-Normal Data: A Simulation Study, Marjorie Howard
Theses and Dissertations
Linear regression is a widely used method for analysis that is well understood across a wide variety of disciplines. In order to use linear regression, a number of assumptions must be met. These assumptions, specifically normality and homoscedasticity of the error distribution can at best be met only approximately with real data. Quantile regression requires fewer assumptions, which offers a potential advantage over linear regression. In this simulation study, we compare the performance of linear (least squares) regression to quantile regression when these assumptions are violated, in order to investigate under what conditions quantile regression becomes the more advantageous method …
Dimension Reduction For Classification With Many Covariates And Pathway Activity Level Estimation,
2018
University of South Carolina
Dimension Reduction For Classification With Many Covariates And Pathway Activity Level Estimation, Seungchul Baek
Theses and Dissertations
The development of science and technology has enabled the use of more covariates. As a result, it has become more difficult to identify dependencies among many covariates. Dimension reduction provides an efficient way to handle this issue by summarizing the effect of covariates via a few linear combinations of covariates. In this dissertation, two methodologies for real-life problems are provided by using dimension reduction equipped with semiparametric theory. The use of semiparametrics allows maximal flexibility of the model by letting some features of the model completely unspecified, while we still enjoy the interpretability of the model through estimating the parameters …
