Applied Statistics Commons^™

Generalized Singular Value Decomposition With Additive Components, Stan Lipovetsky

Journal of Modern Applied Statistical Methods

The singular value decomposition (SVD) technique is extended to incorporate the additive components for approximation of a rectangular matrix by the outer products of vectors. While dual vectors of the regular SVD can be expressed one via linear transformation of the other, the modified SVD corresponds to the general linear transformation with the additive part. The method obtained can be related to the family of principal component and correspondence analyses, and can be reduced to an eigenproblem of a specific transformation of a data matrix. This technique is applied to constructing dual eigenvectors for data visualizing in a two dimensional …

Almost Unbiased Estimator Using Known Value Of Population Parameter(S) In Sample Surveys, Rajesh Singh, S.B. Gupta, Sachin Malik

Journal of Modern Applied Statistical Methods

An almost unbiased estimator using known value of some population parameter(s) is proposed. A class of estimators is defined which includes Singh and Solanki (2012) and Sahai and Ray (1980), Sisodiya and Dwivedi (1981), Singh, Cauhan, Sawan, and Smarandache (2007), Upadhyaya and Singh (1984), Singh and Tailor (2003) estimators. Under simple random sampling without replacement (SRSWOR) scheme the expressions for bias and mean square error (MSE) are derived. Numerical illustrations are given.

A Comparison Of Estimation Methods For Nonlinear Mixed-Effects Models Under Model Misspecification And Data Sparseness: A Simulation Study, Jeffrey R. Harring, Junhui Liu

Journal of Modern Applied Statistical Methods

A Monte Carlo simulation is employed to investigate the performance of five estimation methods of nonlinear mixed effects models in terms of parameter recovery and efficiency of both regression coefficients and variance/covariance parameters under varying levels of data sparseness and model misspecification.

Variable Selection In Regression Using Multilayer Feedforward Network, Tejaswi S. Kamble, Dattatraya N. Kashid

Journal of Modern Applied Statistical Methods

The selection of relevant variables in the model is one of the important problems in regression analysis. Recently, a few methods were developed based on a model free approach. A multilayer feedforward neural network model was proposed for developing variable selection in regression. A simulation study and real data were used for evaluating the performance of proposed method in the presence of outliers, and multicollinearity.

Jmasm39: Algorithm For Combining Robust And Bootstrap In Multiple Linear Model Regression (Sas), Wan Muhamad Amir, Mohamad Shafiq, Hanafi A.Rahim, Puspa Liza, Azlida Aleng, Zailani Abdullah

Journal of Modern Applied Statistical Methods

The aim of bootstrapping is to approximate the sampling distribution of some estimator. An algorithm for combining method is given in SAS, along with applications and visualizations.

Jmasm35: A Percentile-Based Power Method: Simulating Multivariate Non-Normal Continuous Distributions (Sas), Jennifer Koran, Todd C. Headrick

Journal of Modern Applied Statistical Methods

The conventional power method transformation is a moment-matching technique that simulates non-normal distributions with controlled measures of skew and kurtosis. The percentile-based power method is an alternative that uses the percentiles of a distribution in lieu of moments. This article presents a SAS/IML macro that implements the percentile-based power method.

Identification Of Biomarkers For The Overall Survival Of Ovarian Cancer Patients, Kristi Mai

Graduate Theses and Dissertations

Rapid advance in sequencing technology has led to genome-wide analysis of genetic and epigenetic features simultaneously, making it possible to understand the biological mechanisms underlying cancer initiation and progression. However, how to identify important prognostic features poses a great challenge for both statistical modeling and computing. In this thesis, a network-based approach is applied to the Cancer Genome Atlas (TCGA) ovarian cancer data to identify important genes related to the overall survival of ovarian cancer patients. In the first step, a stepwise correlation-based selector is used to reduce the dimensionality of TCGA data, by filtering out a large number of …

Spread Trading In Corn Futures Market, Ryan D. Napier

Graduate Theses and Dissertations

The non-linear relationship between old crop – new crop year spreads in corn futures market and stock-to-use (S-U) ratios published by the United States Department of Agriculture is analyzed. Using a non-linear logarithmic smooth transition regression (LSTR) model, we capture asymmetric market behaviors in high and low S-U regimes. Capturing this relationship and understanding the non-linear aspects of the relationship is of interest of grain merchandizers and speculators in the market. A spread trading strategy is simulated for the sample period, January 1985 through April 2015, to determine if the non-linear relationship is a profitable arbitrage opportunity in the market.

Statistical Modeling Of The Temporal Dynamics In A Large Scale-Citation Network, Luis Javier Ek Jr.

Graduate Theses and Dissertations

Citation Networks of papers are vast networks that grow over time. The manner or the form a citation network grows is not entirely a random process, but a preferential attachment relationship; highly cited papers are more likely to be cited by newly published papers. The result is a network whose degree distribution follows a power law. This growth of citation network of papers will be modeled with a negative binomial regression coupled with logistic growth and/or Cauchy distribution curve. Then a Barabasi-Albert model, based on the negative binomial models, and a combination of the Dirichlet distribution and multinomial will be …

Risk Estimation Toward A Natural History Model For Low Grade Glioma Patients, Anh Thi Hoang Pham

Graduate Theses and Dissertations

Glioma is a common type of primary brain tumor that represents 28% of all brain tumors and 80% of malignant tumors. According to a recent study by the Centers for Disease Control and Prevention (CDC), gliomas account for 53%, 35% and 29% of all brain tumors (68%, 74% and 81% of malignant brain tumors) among children (aged 0-14), teenagers (aged 15-19) and young adults, respectively. Gliomas are often diagnosed through radiological imaging and histopathology. There are two main groups of gliomas following World Health Organization’s classification: Low grade gliomas (LGG), or grade I and II gliomas; and high grade gliomas …

Flesch-Kincaid Reading Grade Level Re-Examined: Creating A Uniform Method For Calculating Readability On A Certification Exam, Emily Neuhoff, Kristiana M. Feeser, Kayla Sutherland, Thomas Hovatter

Online Journal for Workforce Education and Development

Abstract

Objective: This study attempted to establish a consistent measurement technique of the readability of a state-wide Certified Nursing Assistant’s (CNA) certification exam. Background: Monitoring the readability level of an exam helps ensure all test versions do not exceed the maximum reading level of the exam, and that knowledge of the subject matter, rather than reading ability, is being assessed. Method: A two part approach was used to specify and evaluate readability. First, two methods (Microsoft Word^® (MSW) software and published readability formulae) were used to calculate Flesch Reading Ease (FRE) and Flesch-Kincaid Reading Grade Level (FKRGL) for multiple …

The Reliability Of Crowdsourcing: Latent Trait Modeling With Mechanical Turk, Matt Baucum, Steven Rouse Dr., Cindy Miller-Perrin, Elizabeth Mancuso Dr.

Seaver College Research And Scholarly Achievement Symposium

Mechanical Turk, an online crowdsourcing platform, has recently received increased attention in the social sciences as studies continue to suggest its viability as a source for reliable experimental data. Given the ease with which large samples can be quickly and inexpensively gathered, it is worth examining whether Mechanical Turk can provide accurate experimental data for methodologies requiring such large samples. One such methodology is Item Response Theory, a psychometric paradigm that defines test items by a mathematical relationship between a respondent’s ability and the probability of item endorsement. To test whether Mechanical Turk can serve as a reliable source of …

Bayesian Rank Based Methods For Linear And Generalized Linear Models, James Kodzo Dzikunu

Dissertations

A Bayesian Rank Based Method for linear models is developed in this research. The estimation of the regression coefficients is based on the full conditional distributions utilizing a rank based initial fit. The data likelihood is based on the asymptotic distribution of the gradient function and the asymptotic linearity of this rank-based procedure. Prior distributions are put on regression coefficient(s) and scale parameter(s). The effects of different priors on this scale parameter(s) are studied. Using these full conditional distributions, the estimates are obtained by a Markov Chain Monte-Carlo (MCMC) procedure. The results of our simulation studies show that these Bayesian …

Empirical Evaluation Of Different Features Of Design In Confirmatory Factor Analysis, Deyab Almaleki

Dissertations

Factor analysis (FA) is the study of variance within a group. Within-subject variance (WSV) is affected by multiple features in a study context, such as: the study experimental design (ED) and sampling design (SD), thus anything that influences or changes variance may affect the conclusions related to FA.

The aim of this study was to provide empirical evaluation of the influence of different aspects of ED and SD on WSV in the context of FA in terms of model precision and model estimate stability. Four Monte Carlo population correlation matrices were hypothesized based on different communality magnitudes (high, moderate, low, …

A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu

Faculty Publications

Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models is varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: (1) problem module, (2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, (3) meta-learner module which compares the performance of instance-based …

Strengthening Instrumental Variables Through Weighting, Douglas Lehmann, Yun Li, Rajiv Saran, Yi Li

The University of Michigan Department of Biostatistics Working Paper Series

Instrumental variable (IV) methods are widely used to deal with the issue of unmeasured confounding and are becoming popular in health and medical research. IV models are able to obtain consistent estimates in the presence of unmeasured confounding, but rely on assumptions that are hard to verify and often criticized. An instrument is a variable that influences or encourages individuals toward a particular treatment without directly affecting the outcome. Estimates obtained using instruments with a weak influence over the treatment are known to have larger small-sample bias and to be less robust to the critical IV assumption that the instrument …

Evaluating The Impact Of A Hiv Low-Risk Express Care Task-Shifting Program: A Case Study Of The Targeted Learning Roadmap, Linh Tran, Constantin T. Yiannoutsos, Beverly S. Musick, Kara K. Wools-Kaloustian, Abraham Siika, Sylvester Kimaiyo, Mark J. Van Der Laan, Maya L. Petersen

U.C. Berkeley Division of Biostatistics Working Paper Series

In conducting studies on an exposure of interest, a systematic roadmap should be applied for translating causal questions into statistical analyses and interpreting the results. In this paper we describe an application of one such roadmap applied to estimating the joint effect of both time to availability of a nurse-based triage system (low risk express care (LREC)) and individual enrollment in the program among HIV patients in East Africa. Our study population is comprised of 16;513 subjects found eligible for this task-shifting program within 15 clinics in Kenya between 2006 and 2009, with each clinic starting the LREC program between …

Modeled And Measured Image-Plane Polychromatic Speckle Contrast, Noah R. Van Zandt, Jack E. Mccrae, Steven T. Fiorino

Faculty Publications

The statistical properties of speckle relevant to short- to medium-range (tactical) active tracking involving polychromatic illumination are investigated. A numerical model is developed to allow rapid simulation of speckled images including the speckle contrast reduction effects of illuminator bandwidth, surface slope, and roughness, and the polarization properties of both the source and the reflection. Regarding surface slope (relative orientation of the surface normal and illumination/observation directions), Huntley’s theory for speckle contrast, which employs geometrical approximations to decrease computation time, is modified to increase accuracy by incorporation of a geometrical correction factor and better treatment of roughness and polarization. The resulting …

Models For Hsv Shedding Must Account For Two Levels Of Overdispersion, Amalia Magaret

UW Biostatistics Working Paper Series

We have frequently implemented crossover studies to evaluate new therapeutic interventions for genital herpes simplex virus infection. The outcome measured to assess the efficacy of interventions on herpes disease severity is the viral shedding rate, defined as the frequency of detection of HSV on the genital skin and mucosa. We performed a simulation study to ascertain whether our standard model, which we have used previously, was appropriately considering all the necessary features of the shedding data to provide correct inference. We simulated shedding data under our standard, validated assumptions and assessed the ability of 5 different models to reproduce the …

Thermally Induced Aggregation Of Rigid Spheres On A Liquid Surface, Eric Forgoston, Leo Hentschker, Siobhan Soltau, Patrick Truitt, Ashuwin Vaidya

Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works

Fluids provide the optimal setting to explore natural patterns far from thermodynamic equilibrium. Experiments suggest that randomly dispersed particles on a liquid surface tend to aggregate on the surface of liquid over time, and the process is enhanced by an increase in the temperature of the liquid. We show that the agglomeration radii increases monotonically with temperature up until the point where all particles in the system form a single, large aggregate. The aggregation dynamics is related to changes in the material properties of the liquid including its viscosity and surface tension as well as the convection driven flow generated …