Aggregated Quantitative Multifactor Dimensionality Reduction,
2016
University of Kentucky
Aggregated Quantitative Multifactor Dimensionality Reduction, Rebecca E. Crouch
Theses and Dissertations--Statistics
We consider the problem of making predictions for quantitative phenotypes based on gene-to-gene interactions among selected Single Nucleotide Polymorphisms (SNPs). Previously, Quantitative Multifactor Dimensionality Reduction (QMDR) has been applied to detect gene-to-gene interactions associated with elevated quantitative phenotypes, by creating a dichotomous predictor from one interaction which has been deemed optimal. We propose an Aggregated Quantitative Multifactor Dimensionality Reduction (AQMDR), which exhaustively considers all k-way interactions among a set of SNPs and replaces the dichotomous predictor from QMDR with a continuous aggregated score. We evaluate this new AQMDR method in a series of simulations for two-way and three-way interactions, …
Improved Parameter Estimation Of The Log-Logistic Distribution With Applications,
2016
Michigan Technological University
Improved Parameter Estimation Of The Log-Logistic Distribution With Applications, Joseph Reath
Dissertations, Master's Theses and Master's Reports
In this report, we work with parameter estimation of the log-logistic distribution. We first consider one of the most common methods encountered in the literature, the maximum likelihood (ML) method. However, it is widely known that the maximum likelihood estimators (MLEs) are usually biased with a finite sample size. This motivates a study of obtaining unbiased or nearly unbiased estimators for this distribution. Specifically, we consider a certain `corrective' approach and Efron's bootstrap resampling method, which both can reduce the biases of the MLEs to the second order of magnitude. As a comparison, we also consider the generalized moments (GM) …
Doing Qualitative Research Online Book Review,
2016
Walden University
Doing Qualitative Research Online Book Review, Donna M. Busarow
Journal of Social, Behavioral, and Health Sciences
A recent addition to qualitative instruction is Salmon's (2016) book, Doing Qualitative Research Online. This book review examines the book as an educational tool for student researchers.
Dimension Reduction And Variable Selection,
2016
Virginia Commonwealth University
Dimension Reduction And Variable Selection, Hossein Moradi Rekabdarkolaee
Theses and Dissertations
High-dimensional data are becoming increasingly available as data collection technology advances. Over the last decade, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics, signal processing, and environmental studies. Statistical techniques such as dimension reduction and variable selection play important roles in high dimensional data analysis. Sufficient dimension reduction provides a way to find the reduced space of the original space without a parametric model. This method has been widely applied in many scientific fields such as genetics, brain imaging analysis, econometrics, environmental sciences, etc. …
A New Right Tailed Test Of The Ratio Of Variances,
2016
University of North Florida
A New Right Tailed Test Of The Ratio Of Variances, Elizabeth Rochelle Lesser
UNF Graduate Theses and Dissertations
It is important to be able to compare variances efficiently and accurately regardless of the parent populations. This study proposes a new right tailed test for the ratio of two variances using the Edgeworth’s expansion. To study the Type I error rate and Power performance, simulation was performed on the new test with various combinations of symmetric and skewed distributions. It is found to have more controlled Type I error rates than the existing tests. Additionally, it also has sufficient power. Therefore, the newly derived test provides a good robust alternative to the already existing methods.
Inequality In Treatment Benefits: Can We Determine If A New Treatment Benefits The Many Or The Few?,
2015
Johns Hopkins University, Bloomberg School of Public Health, Department of Biostatistics
Inequality In Treatment Benefits: Can We Determine If A New Treatment Benefits The Many Or The Few?, Emily Huang, Ethan Fang, Daniel Hanley, Michael Rosenblum
Johns Hopkins University, Dept. of Biostatistics Working Papers
The primary analysis in many randomized controlled trials focuses on the average treatment effect and does not address whether treatment benefits are widespread or limited to a select few. This problem affects many disease areas, since it stems from how randomized trials, often the gold standard for evaluating treatments, are designed and analyzed. Our goal is to learn about the fraction who benefit from a treatment, based on randomized trial data. We consider the case where the outcome is ordinal, with binary outcomes as a special case. In general, the fraction who benefit is a non-identifiable parameter, and the best …
Bayes Multiple Binary Classifier - How To Make Decisions Like A Bayesian,
2015
Florida International University
Bayes Multiple Binary Classifier - How To Make Decisions Like A Bayesian, Wensong Wu
Mathematics Colloquium Series
This presentation will start by a general introduction of Bayesian statistics, which has become popular in the era of big data. Then we consider a two-class classification problem, where the goal is to predict the class membership of M units based on the values of high-dimensional categorical predictor variables as well as both the values of predictor variables and the class membership of other N independent units. We focus on applying generalized linear regression models with Boolean expressions of categorical predictors. We consider a Bayesian and decision-theoretic framework, and develop a general form of Bayes multiple binary classification functions with …
Structural Properties Of Transmuted Weibull Distribution,
2015
University of Kashmir, Srinagar, India
Structural Properties Of Transmuted Weibull Distribution, Kaisar Ahmad, S. P. Ahmad, A. Ahmed
Journal of Modern Applied Statistical Methods
The transmuted Weibull distribution, and a related special case, is introduced. Estimates of parameters are obtained by using a new method of moments.
New Entropy Estimators With Smaller Root Mean Squared Error,
2015
Al al-Bayt University, Mafraq, Jordan
New Entropy Estimators With Smaller Root Mean Squared Error, Amer Ibrahim Al-Omari
Journal of Modern Applied Statistical Methods
New estimators of entropy of continuous random variable are suggested. The proposed estimators are investigated under simple random sampling (SRS), ranked set sampling (RSS), and double ranked set sampling (DRSS) methods. The estimators are compared with Vasicek (1976) and Al-Omari (2014) entropy estimators theoretically and by simulation in terms of the root mean squared error (RMSE) and bias values. The results indicate that the suggested estimators have less RMSE and bias values than their competing estimators introduced by Vasicek (1976) and Al-Omari (2014).
An Empirical Study On Different Ranking Methods For Effective Data Classification,
2015
K.L.N. College of Engineering, Madurai, India
An Empirical Study On Different Ranking Methods For Effective Data Classification, Ilangovan Sangaiah, A. Vincent Antony Kumar, Appavu Balamurugan
Journal of Modern Applied Statistical Methods
Ranking is the attribute selection technique used in the pre-processing phase to emphasize the most relevant attributes which allow models of classification simpler and easy to understand. It is a very important and a central task for information retrieval, such as web search engines, recommendation systems, and advertisement systems. A comparison between eight ranking methods was conducted. Ten different learning algorithms (NaiveBayes, J48, SMO, JRIP, Decision table, RandomForest, Multilayerperceptron, Kstar) were used to test the accuracy. The ranking methods with different supervised learning algorithms give different results for balanced accuracy. It was shown the selection of ranking methods could be …
Caution For Software Use Of New Statistical Methods (R),
2015
Dallas Independent School District, Dallas, TX
Caution For Software Use Of New Statistical Methods (R), Akiva J. Lorenz, Barry S. Markman, Shlomo Sawilowsky
Journal of Modern Applied Statistical Methods
Open source programming languages such as R allow statisticians to develop and rapidly disseminate advanced procedures, but sometimes at the expense of a proper vetting process. A new example is the least trimmed squares regression available in R’s lqs() in the MASS library. It produces pretty regression lines, particularly in the presence of outliers. However, this procedure lacks a defined standard error, and thus it should be avoided.
Inferences About The Skipped Correlation Coefficient: Dealing With Heteroscedasticity And Non-Normality,
2015
University of Southern California
Inferences About The Skipped Correlation Coefficient: Dealing With Heteroscedasticity And Non-Normality, Rand Wilcox
Journal of Modern Applied Statistical Methods
A common goal is testing the hypothesis that Pearson’s correlation is zero and typically this is done based on Student’s T test. There are, however, several well-known concerns. First, Student’s T is sensitive to heteroscedasticity. That is, when it rejects, it is reasonable to conclude that there is dependence, but in terms of making a decision about the strength of the association, it is unsatisfactory. Second, Pearson’s correlation is not robust: it can poorly reflect the strength of the association. Even a single outlier can have a tremendous impact on the usual estimate of Pearson’s correlation, which can result in …
Front Matter,
2015
Wayne State University
Jmasm34: Two Group Program For Cohen's D, Hedges’ G, Η2, Radj2, Ω2, Ɛ2, Confidence Intervals, And Power,
2015
Northern Illinois University
Jmasm34: Two Group Program For Cohen's D, Hedges’ G, Η2, Radj2, Ω2, Ɛ2, Confidence Intervals, And Power, David A. Walker
Journal of Modern Applied Statistical Methods
The purpose of this research is to provide an application for users interested in a SPSS syntax program to determine an array of commonly-employed effect sizes and confidence intervals not readily available in SPSS functionality, such as the standardized mean difference and r-related squared indices, for a between-group design.
Monte Carlo Comparison Of The Parameter Estimation Methods For The Two-Parameter Gumbel Distribution,
2015
Sinop University
Monte Carlo Comparison Of The Parameter Estimation Methods For The Two-Parameter Gumbel Distribution, Demet Aydin, Birdal Şenoğlu
Journal of Modern Applied Statistical Methods
The performances of the seven different parameter estimation methods for the Gumbel distribution are compared with numerical simulations. Estimation methods used in this study are the method of moments (ME), the method of maximum likelihood (ML), the method of modified maximum likelihood (MML), the method of least squares (LS), the method of weighted least squares (WLS), the method of percentile (PE) and the method of probability weighted moments (PWM). Performance of the estimators is compared with respect to their biases, MSE and deficiency (Def) values via Monte-Carlo simulation. A Monte Carlo Simulation study showed that the method of PWM was …
Contrails: Causal Inference Using Propensity Scores,
2015
twobluecats.com
Contrails: Causal Inference Using Propensity Scores, Dean S. Barron
Journal of Modern Applied Statistical Methods
Contrails are clouds caused by airplane exhausts, which geologists contend decrease daily temperature ranges on Earth. Following the 2001 World Trade Center attack, cancelled domestic flights triggered the first absence of contrails in decades. Resultant exceptional data capacitated causal inference analysis by propensity score matching. Estimated contrail effect was 6.8981°F.
Two Stage Robust Ridge Method In A Linear Regression Model,
2015
Ladoke Akintola University of Technology
Two Stage Robust Ridge Method In A Linear Regression Model, Adewale Folaranmi Lukman, Oyedeji Isola Osowole, Kayode Ayinde
Journal of Modern Applied Statistical Methods
Two Stage Robust Ridge Estimators based on robust estimators M, MM, S, LTS are examined in the presence of autocorrelation, multicollinearity and outliers as alternative to Ordinary Least Square Estimator (OLS). The estimator based on S estimator performs better. Mean square error was used as a criterion for examining the performances of these estimators.
The Bayes Factor For Case-Control Studies With Misclassified Data,
2015
Centers for Disease Control & Prevention
The Bayes Factor For Case-Control Studies With Misclassified Data, Tzesan Lee
Journal of Modern Applied Statistical Methods
The question of how to test if collected data for a case-control study are misclassified was investigated. A mixed approach was employed to calculate the Bayes factor to assess the validity of the null hypothesis of no-misclassification. A real-world data set on the association between lung cancer and smoking status was used as an example to illustrate the proposed method.
Resolving The Issue Of How Reliability Is Related To Statistical Power: Adhering To Mathematical Definitions,
2015
Carleton University
Resolving The Issue Of How Reliability Is Related To Statistical Power: Adhering To Mathematical Definitions, Donald W. Zimmerman, Bruno D. Zumbo
Journal of Modern Applied Statistical Methods
Reliability in classical test theory is a population-dependent concept, defined as a ratio of true-score variance and observed-score variance, where observed-score variance is a sum of true and error components. On the other hand, the power of a statistical significance test is a function of the total variance, irrespective of its decomposition into true and error components. For that reason, the reliability of a dependent variable is a function of the ratio of true-score variance and observed-score variance, whereas statistical power is a function of the sum of the same two variances. Controversies about how reliability is related to statistical …
In (Partial) Defense Of .05,
2015
University of Rochester and The Ohio State University
In (Partial) Defense Of .05, Thomas R. Knapp
Journal of Modern Applied Statistical Methods
Researchers are frequently chided for choosing the .05 alpha level as the determiner of statistical significance (or non-significance). A partial justification is provided.
