Digital Commons Network^™

Statistician Recommends A Dose Of Skepticism, Aldemaro Romero Jr.

Publications and Research

No abstract provided.

Neath Studies, Teaches The Uncertainties Of Life, Aldemaro Romero Jr.

Publications and Research

No abstract provided.

Approximate Vs. Monte Carlo Critical Values For The Winsorized T-Test, Michael Lance

Wayne State University Dissertations

Historically, it has been accepted practice for critical values for the Winsorized t test for independent samples to be based on adjusted degrees of freedom depending on the number of total non-Winsorized (approximate) values. Recently, a new such table of Winsorized critical values has been developed via approximate randomization by Monte Carlo simulation.

Based on eight common data distributions estimated from Psychology and Education along with the normal and five Mathematical distributions, these two tables of values were compared with respect to robustness to types I and II errors through Monte Carlo simulations for one and 10% Winsorized values per …

A Comparison Of The Effects Of Non-Normal Distributions On Tests Of Equivalence, Linda Ellington

Wayne State University Dissertations

Statistical theory and its application provide the foundation to modern systematic inquiry in the behavioral, physical and social sciences disciplines (Fisher, 1958; Wilcox, 1996). It provides the tools for scholars and researchers to operationalize constructs, describe populations, and measure and interpret the relations between populations and variables (Weinbach & Grinnell, 1997; Wilcox, 1996). Given that the majority of real data analysis in the behavioral and social sciences is comprised of non-normally distributed data, it is important that researchers be aware of the effects of non-normal distributions on the probability of detecting equivalence between populations.

The present study examined the effects …

The Doubly Inflated Poisson And Related Regression Models, Manasi Sheth-Chandra

Mathematics & Statistics Theses & Dissertations

Most real life count data consists of some values that are more frequent than allowed by the common parametric families of distributions. For data consisting of only excess zeros, in a seminal paper Lambert (1992) introduced Zero-Inflated Poisson (ZIP) model, which is a mixture model that accounts for the inflated zeros. In this thesis, two Doubly Inflated Poisson (DIP) probability models, DIP (p, λ) and DIP ( p1, p2, λ), are discussed for situations where there is another inflated value k > 0 besides the inflated zeros. The distributional properties such as identifiability, moments, and conditional probabilities …

On The Use Of Log-Transformation Vs. Nonlinear Regression For Analyzing Biological Power-Laws, Xiao Xiao

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Power-law relationships are among the most well-studied functional relationships in biology . Recently the common practice of fitting power-laws using linear regression on log-transformed data (LR) has been criticized, calling into question the conclusions of hundreds of studies. It has been suggested that nonlinear regression (NLR) is preferable, but no rigorous comparison of these two methods has been conducted. Using Monte Carlo simulations we demonstrate that the error distribution determines which method performs better, with LR better characterizing data with multiplicative lognormal error and NLR better characterizing data with additive normal error. Analysis of 471 biological power-laws shows that both …

Identifying Rare Variants From Exome Scans: The Gaw17 Experience, Saurabh Ghosh, Heike Bickeboller, Julia Bailey, Joan E. Bailey-Wilson, Rita Cantor, Robert Culverhouse, Warwick Daw, Anita L. Destefano, Corinne D. Engelman, Anthony Hinrichs, Jeanine Houwing-Duistermaat, Inke R. Konig, Jack Kent, Nan Laird, Nathan Pankratz, Andrew Paterson, Elizabeth Pugh, Brian Suarez, Yan Sun, Alun Thomas, Nathan L. Tintle, Xiaofeng Zhu, Andreas Ziegler, Jean W. Maccluer, Laura Almasy

Faculty Work Comprehensive List

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop

An Analog Experiment Comparing Goal-Free Evaluation And Goal Achievement Evaluation Utility, Brandon W. Youker

Dissertations

Goal-free evaluation (GFE) is the process of determining the merit of an evaluand independent of the stated or implied goals and objectives, whereas goal achievement evaluation (GAE), as the most rudimentary form of goal-based evaluation, determines merit according to the evaluand’s level of accomplishment with regard to its goals. This study examines the utility of GAE and GFE from the perspective of the evaluation’s intended users. In the study, two evaluation teams, goal achievement and goal-free, independently and simultaneously evaluate the same human service program. Each team produced a final evaluation report, which was read by the evaluation’s users, who …

Class Discovery And Prediction Of Tumor With Microarray Data, Bo Liu

All Graduate Theses, Dissertations, and Other Capstone Projects

Current microarray technology is able take a single tissue sample to construct an Affymetrix oglionucleotide array containing (estimated) expression levels of thousands of different genes for that tissue. The objective is to develop a more systematic approach to cancer classification based on Affymetrix oglionucleotide microarrays. For this purpose, I studied published colon cancer microarray data. Colon cancer, with 655,000 deaths worldwide per year, has become the fourth most common form of cancer in the United States and the third leading cause of cancer - related death in the Western world. This research has been focuses in two areas: class discovery, …

Bayesian Computational Methods For Hidden Markov Models, Samson Laine Ghebremariam

Open Access Theses & Dissertations

Hidden Markov Models (HMMs) have been applied to many real-world problems. Hidden Markov modeling has recently become increasingly important and popular among researchers,and many software tools are based on them. Given that the models are rich in mathematical structure, they can form theoretical foundation for use in a wide range of applications. Hidden Markov models provide a universal configuration for statistical analysis of a large variety of DNA sequences containing symbols A, C, G, T. In a HMM, it is impossible to figure out what state the model is in by just having a look at the symbol generated.

A …

Gamma And Generalized Gamma Distributions, Victor Hugo Jiménez Nava

Open Access Theses & Dissertations

We present the Generalized Gamma Distribution, study its properties and derive the estimators of the parameters. This distribution includes many standard forms, like: Gamma, Exponential, Weibull, Half Normal and others. Sum and ratios of independent generalized gamma lead to intractable forms. However approximation works well. In particular two-moment gamma and three moment normal approximations are shown to approximate the sum of k independent gamma as well as generalized gamma distributions.

Computational Methods Of Hidden Markov Models With Respect To Cpg Island Prediction In Dna Sequences, Roberto Angel Ortega

Open Access Theses & Dissertations

Hidden Markov models (HMM's) are a specific case of Markov models where, contrary to Markov chains, the observer is unaware of what state the model was in when the symbol is observed. Like Markov chains, HMM's assume that the future state of a sequence is dependent only on the current state of the sequence. The parameters associated with HMM's are transition and emission probabilities, where transition probabilities are associated with the probability of transitioning from one state to another, and emission probabilities are the probabilities associated with observing a symbol given it came from a specific state.

The structure of …

Estimating The Effect Of Dust And Low Wind Events On Hospitalizations For Asthma While Adjusting For Hourly Levels Of Air Pollutants, Priyangi Kanchana Bulathsinhala

Open Access Theses & Dissertations

El Paso, Texas is known as one of the dust hotspots in North America. We explore the effect of dust and low wind events on asthma admissions in El Paso, Texas between 2000 and 2005. Conditional logistic regression with a case-crossover design was used to estimate the probability of hospitalization after dust and low wind events while controlling for pollutants with hourly monitor measurements, and weather. The historical functional linear model is used to incorporate the hourly pollutant measures into the regression model with a continuous lag, as an alternative to a distributed lag model based on daily averages. The …

Estimating The Reproductive Numbers For The 2008-2009 Cholera Outbreaks In Zimbabwe, Zindoga Mukandavire, Shu Liao, Jin Wang, Holly Gaff, David L. Smith, J. Glenn Morris Jr.

Biological Sciences Faculty Publications

Cholera remains an important global cause of morbidity and mortality, capable of causing periodic epidemic disease. Beginning in August 2008, a major cholera epidemic occurred in Zimbabwe, with 98,585 reported cases and 4,287 deaths. The dynamics of such outbreaks, particularly in nonestuarine regions, are not well understood. We explored the utility of mathematical models in understanding transmission dynamics of cholera and in assessing the magnitude of interventions necessary to control epidemic disease. Weekly data on reported cholera cases were obtained from the Zimbabwe Ministry of Health and Child Welfare (MoHCW) for the period from November 13, 2008 to July 31, …

Fact Sheet, Yale University Office Of Institutional Research

Fact Sheets

No abstract provided.

Meta-Analysis Of Single-Case Data: A Monte Carlo Investigation Of A Three Level Model, Corina M. Owens

USF Tampa Graduate Theses and Dissertations

Numerous ways to meta-analyze single-case data have been proposed in the literature, however, consensus on the most appropriate method has not been reached. One method that has been proposed involves multilevel modeling. This study used Monte Carlo methods to examine the appropriateness of Van den Noortgate and Onghena's (2008) raw data multilevel modeling approach to the meta-analysis of single-case data. Specifically, the study examined the fixed effects (i.e., the overall average baseline level and the overall average treatment effect) and the variance components (e.g., the between person within study variance in the average baseline level, the between study variance in …

Parent Involvement In Children's Schooling: An Investigation Of Measurement Equivalence Across Ethnic Groups, Heather Marie Scott

USF Tampa Graduate Theses and Dissertations

Epstein et al.'s Theory of Overlapping Spheres of Influence focuses on the interaction and communication, or partnerships, among families, schools, and the community to bring the three closer together. The theory works in conjunction with Epstein's typology of parental involvement, which focuses on six types of involvement that are instrumental to a child's development and his/her school and educational success. These serve as the framework for the study and support the construct of parent's involvement in children's schooling.

The purpose of the current study was to conduct further validation analyses of an inventory designed to measure the construct of parent …

Statistical Analysis And Modeling: Cancer, Clinical Trials, Environment And Epidemiology., Dimitrios Vovoras

USF Tampa Graduate Theses and Dissertations

The current thesis is structured in four parts. Vector smoothing methods are used to study environmental data, in particular records of extreme precipitation, the models utilized belong to the vector generalized additive class. In the statistical analysis of observational studies the identification and adjustment for prognostic factors is an important component of the analysis; employing flexible statistical methods to identify and characterize the effect of potential prognostic factors in a clinical trial, namely "generalized additive models", presents an alternative to the traditional linear statistical model. The classes of models for which the methodology gives generalized additive extensions include grouped survival …

Modeling Endogenous Treatment Eects With Heterogeneity: A Bayesian Nonparametric Approach, Xuequn Hu

USF Tampa Graduate Theses and Dissertations

This dissertation explores the estimation of endogenous treatment effects in the presence of heterogeneous responses. A Bayesian Nonparametric approach is taken to model the heterogeneity in treatment effects. Specifically, I adopt the Dirichlet Process Mixture (DPM) model to capture the heterogeneity and show that DPM often outperforms Finite Mixture Model (FMM) in providing more flexible function forms and thus better model fit. Rather than fixing the number of components in a mixture model, DPM allows the data and prior knowledge to determine the number of components in the data, thus providing an automatic mechanism for model selection.

Two DPM models …

Evaluating Methods For The Analysis Of Rare Variants In Sequence Data, Alexander Luedtke, Scott Powers, Ashley Petersen, Alexandra Sitarik, Airat Bekmetjev, Nathan L. Tintle

Faculty Work Comprehensive List

A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying …

Inflated Type I Error Rates When Using Aggregation Methods To Analyze Rare Variants In The 1000 Genomes Project Exon Sequencing Data In Unrelated Individuals: Summary Results From Group 7 At Genetic Analysis Workshop 17, Nathan L. Tintle, Hugues Aschard, Inchi Hu, Nora Nock, Haitian Wang, Elizabeth Pugh

Faculty Work Comprehensive List

As part of Genetic Analysis Workshop 17 (GAW17), our group considered the application of novel and standard approaches to the analysis of genotype-phenotype association in next-generation sequencing data. Our group identified a major issue in the analysis of the GAW17 next-generation sequencing data: type I error and false-positive report probability rates higher than those expected based on empirical type I error levels (as high as 90%). Two main causes emerged: population stratification and long-range correlation (gametic phase disequilibrium) between rare variants. Population stratification was expected because of the diverse sample. Correlation between rare variants was attributable to both random causes …

Identification Of Genetic Association Of Multiple Rare Variants Using Collapsing Methods, Yan V. Sun, Yun Ju Sung, Nathan L. Tintle, Andreas Ziegler

Faculty Work Comprehensive List

Next-generation sequencing technology allows investigation of both common and rare variants in humans. Exomes are sequenced on the population level or in families to further study the genetics of human diseases. Genetic Analysis Workshop 17 (GAW17) provided exomic data from the 1000 Genomes Project and simulated phenotypes. These data enabled evaluations of existing and newly developed statistical methods for rare variant sequence analysis for which standard statistical methods fail because of the rareness of the alleles. Various alternative approaches have been proposed that overcome the rareness problem by combining multiple rare variants within a gene. These approaches are termed collapsing …

Evaluating Methods For Combining Rare Variant Data In Pathway-Based Tests Of Genetic Association, Ashley Petersen, Alexandra Sitarik, Alexander Luedtke, Scott Powers, Airat Bekmetjev, Nathan L. Tintle

Faculty Work Comprehensive List

Analyzing sets of genes in genome-wide association studies is a relatively new approach that aims to capitalize on biological knowledge about the interactions of genes in biological pathways. This approach, called pathway analysis or gene set analysis, has not yet been applied to the analysis of rare variants. Applying pathway analysis to rare variants offers two competing approaches. In the first approach rare variant statistics are used to generate p-values for each gene (e.g., combined multivariate collapsing [CMC] or weighted-sum [WS]) and the gene-level p-values are combined using standard pathway analysis methods (e.g., gene set enrichment analysis or …

Modeling And Analysis Of Repeated Ordinal Data Using Copula Based Likelihoods And Estimating Equation Methods, Raghavendra Rao Kurada

Mathematics & Statistics Theses & Dissertations

Repeated or longitudinal ordinal data occur in many fields such as biology, epidemiology, and finance. These data normally are analyzed using both likelihood and non-likelihood methods. The first part of this dissertation discusses the multivariate ordered probit model which is a likelihood method based on latent variables. We show that this latent variable model belong to a very general class of Copula models. We use the copula representation for the multivariate ordered probit model to obtain maximum likelihood estimates of the parameters. We apply the methodology in the analysis of real life data examples.

Though likelihood methods are preferable, there …

Why Is An Einstein Ring Blue?, Jonathan Blackledge

Articles

Albert Einstein predicted the existence of `Einstein rings' as a consequence of his general theory of relativity. The phenomenon is a direct result of the idea that if a mass warps space-time then light (and other electromagnetic waves) will be `lensed' by the strong gravitational field produced by a large cosmological body such as a galaxy. Since 1998, when the first complete Einstein ring was observed, many more complete or partially complete Einstein rings have been observed in the radio and infrared spectra, for example, and by the Hubble Space Telescope in the optical spectrum. However, in the latter case, …

Drift And The Risk-Free Rate, Anda Gadidov, M. C. Spruill

Faculty Articles

It is proven, under a set of assumptions differing from the usual ones in the unboundedness of the time interval, that, in an economy in equilibrium consisting of a risk-free cash account and an equity whose price process is a geometric Brownian motion on [0,∞), the drift rate must be close to the risk-free rate; if the drift rate μ and the risk-free rate r are constants, then r = μ and the price process is the same under both empirical and risk neutral measures. Contributing in some degree perhaps to interest in this mathematical curiosity is the fact, based …

Robustness Analysis Of Slow Learning In Iterative Learning Control Systems, John R. Singler, Douglas A. Bristow

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper examines robust stability and robust transient growth in Iterative Learning Control (ILC). It is well known that small perturbations in system dynamics can result in very large transient growth of some ILC systems. Even larger perturbations can result in instability. One ad hoc technique commonly employed to improve robustness is to slow the learning rate by reducing the learning filter gain or lowpass filtering the error signal. Here, pseudospectra analysis is used to analyze the robustness of ILC algorithms with slow learning. It is found that robustness bounds can be increased and transient growth decreased with decreasing learning …

Towards Transient Growth Analysis And Design In Iterative Learning Control, Douglas A. Bristow, John R. Singler

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In this article the problem of bounding transient growth in iterative learning control (ILC) is examined. While transient growth is not a desirable property, the alternative, robust monotonic convergence, leads to fundamental performance limitations. to circumvent these limitations, this article considers the possibility that some transient growth, if properly limited, is a viable and practical option. Towards this end, this article proposes tools for analysing worst-case transient growth in ILC. the proposed tools are based on pseudospectra analysis, which is extended to apply to ILC of uncertain systems. Two practical problems in norm-optimal ILC weighting parameter design are considered. Using …

Academic Predictors Of National Council Licensure Examination For Registered Nurses Pass Rates, Maybeth J. Elliott

Walden Dissertations and Doctoral Studies

The United States continues to be affected by a severe, long-standing nursing shortage that is not projected to resolve within the next 10 or more years. Unsuccessful passage of the National Council Licensure Examination for Registered Nurses (NCLEX-RN) among graduate nurses remains one of several key contributors to the nursing shortage. The goal of this study was to identify if either cumulative fall semester GPA; the overall prenursing science, mathematics, and English GPA; type of high school background; TOEFL score; clinical pass or fail; and on-time program completion best predicted passage of NCLEX-RN. Archived records from the academic years of …

Uniform And Partially Uniform Redistribution Rules, Florentin Smarandache, Jean Dezert

Branch Mathematics and Statistics Faculty and Staff Publications

This short paper introduces two new fusion rules for combining quantitative basic belief assignments. These rules although very simple have not been proposed in literature so far and could serve as useful alternatives because of their low computation cost with respect to the recent advanced Proportional Conflict Redistribution rules developed in the DSmT framework.