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Articles 9751 - 9780 of 21237

Full-Text Articles in Social and Behavioral Sciences

Effects Of Ankle Weights On Metabolic Response And Muscle Activity On A Lower Body Positive Pressure Treadmill 2017, Saige Hupman May 2017

Effects Of Ankle Weights On Metabolic Response And Muscle Activity On A Lower Body Positive Pressure Treadmill 2017, Saige Hupman

Master's Theses

Lower body positive pressure (LBPP) treadmills are growing in popularity for rehabilitative use, as the benefits of exercising at partially supported body weight may induce faster recovery. It is unknown if there are certain practices that increase exercise intensity while maintaining positive effects of LBPP. Adding ankle weights when walking or running could increase intensity of rehabilitation programs while maintaining the comfort of supported body weight. PURPOSE: To measure metabolic response (VO2, RER, HR, Caloric expenditure), RPE, and lower limb electromyography (EMG) amplitudes of LBPP treadmill walking and running with and without ankle weights. METHODS: Sixteen participants (Age: 21.94 ± …

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Using Multiple Imputation To Address Missing Values Of Hierarchical Data, Yujia Zhang, Sara Crawford, Sheree Boulet, Michael Monsour, Bruce Cohen, Patricia Mckane, Karen Freeman May 2017

Using Multiple Imputation To Address Missing Values Of Hierarchical Data, Yujia Zhang, Sara Crawford, Sheree Boulet, Michael Monsour, Bruce Cohen, Patricia Mckane, Karen Freeman

Journal of Modern Applied Statistical Methods

Missing data may be a concern for data analysis. If it has a hierarchical or nested structure, the SUDAAN package can be used for multiple imputation. This is illustrated with birth certificate data that was linked to the Centers for Disease Control and Prevention’s National Assisted Reproductive Technology Surveillance System database. The Cox-Iannacchione weighted sequential hot deck method was used to conduct multiple imputation for missing/unknown values of covariates in a logistic model.

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Selection Of Statistical Software For Data Scientists And Teachers, Ceyhun Ozgur, Min Dou, Yang Li, Grace Rogers May 2017

Selection Of Statistical Software For Data Scientists And Teachers, Ceyhun Ozgur, Min Dou, Yang Li, Grace Rogers

Journal of Modern Applied Statistical Methods

The need for analysts with expertise in big data software is becoming more apparent in today’s society. Unfortunately, the demand for these analysts far exceeds the number available. A potential way to combat this shortage is to identify the software sought by employers and to align this with the software taught by universities. This paper will examine multiple data analysis software – Excel add-ins, SPSS, SAS, Minitab, and R – and it will outline the cost, training, statistical methods/tests/uses, and specific uses within industry for each of these software. It will further explain implications for universities and students.

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An Unbiased Estimator Of The Greatest Lower Bound, Nol Bendermacher May 2017

An Unbiased Estimator Of The Greatest Lower Bound, Nol Bendermacher

Journal of Modern Applied Statistical Methods

The greatest lower bound to the reliability of a test, based on a single administration, is the Greatest Lower Bound (GLB). However the estimate is seriously biased. An algorithm is described that corrects this bias.

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Monte Carlo Study Of Some Classification-Based Ridge Parameter Estimators, Adewale Folaranmi Lukman, Kayode Ayinde, Adegoke S. Ajiboye May 2017

Monte Carlo Study Of Some Classification-Based Ridge Parameter Estimators, Adewale Folaranmi Lukman, Kayode Ayinde, Adegoke S. Ajiboye

Journal of Modern Applied Statistical Methods

Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been proposed. In this study, estimators based on Dorugade (2014) and Adnan et al. (2014) were classified into different forms and various types using the idea of Lukman and Ayinde (2015). Some new ridge estimators were proposed. Results shows that the proposed estimators based on Adnan et al. (2014) perform generally better than the existing ones.

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Multivariate Multilevel Modeling Of Age Related Diseases, Kapuruge N. O. Ranathunga, Roshini Sooriyarachchi May 2017

Multivariate Multilevel Modeling Of Age Related Diseases, Kapuruge N. O. Ranathunga, Roshini Sooriyarachchi

Journal of Modern Applied Statistical Methods

The emerging role of modeling multivariate multilevel data in the context of analyzing the risk factors are examined for the severity of cardiovascular disease diabetes, and chronic respiratory conditions. The modeling phase results leads to some important interaction terms between blood glucose, blood pressure, obesity, smoking and alcohol to the mortality rates.

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Analysis Of Robust Parameter Designs, Tak K. Mak, Fassil Nebebe May 2017

Analysis Of Robust Parameter Designs, Tak K. Mak, Fassil Nebebe

Journal of Modern Applied Statistical Methods

The analysis of robust parameter design is discussed via a model incorporating mean-variance relationship which, when ignored as in the classical regression approach, can be problematic. The model is also capable of alleviating the difficulties of the regression approach in the search of the minimum variance occurring region.

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Distribution Fits For Various Parameters In The Florida Public Hurricane Loss Model, Victoria Oxenyuk, Sneh Gulati, B. M. Golam Kibria, Shahid Hamid May 2017

Distribution Fits For Various Parameters In The Florida Public Hurricane Loss Model, Victoria Oxenyuk, Sneh Gulati, B. M. Golam Kibria, Shahid Hamid

Journal of Modern Applied Statistical Methods

The purpose of this study is to re-analyze the atmospheric science component of the Florida Public Hurricane Loss Model v. 5.0, in order to investigate if the distributional fits used for the model parameters could be improved upon. We consider alternate fits for annual hurricane occurrence, radius of maximum winds and the pressure profile parameter.

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Stochastic Model For Cancer Cell Growth Through Single Forward Mutation, Jayabharathiraj Jayabalan May 2017

Stochastic Model For Cancer Cell Growth Through Single Forward Mutation, Jayabharathiraj Jayabalan

Journal of Modern Applied Statistical Methods

A stochastic model for cancer cell growth in any organ is presented, based on a single forward mutation. Cell growth is explained in a one-dimensional stochastic model, and statistical measures for the variable representing the number of malignant cells are derived. A numerical study is conducted to observe the behavior of the model.

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Genetic Algorithms For Cross-Calibration Of Categorical Data, Suja M. Aboukhamseen, Rym A. M'Hallah May 2017

Genetic Algorithms For Cross-Calibration Of Categorical Data, Suja M. Aboukhamseen, Rym A. M'Hallah

Journal of Modern Applied Statistical Methods

The probabilistic problem of cross-calibration of two categorical variables is addressed. A probabilistic forecast of the categorical variables is obtained based on a sample of observed data. This forecast is the output of a genetic algorithm based approach, which makes no assumption on the type of relationship between the two variables and applies a scoring rule to assess the fitness of the chromosomes. It converges to a good-quality point probability forecast of the joint distribution of the two variables. The proposed approach is applied both at stationary points in time and across time. Its performance is enhanced when additional sampled …

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A Schmid-Leiman-Based Transformation Resulting In Perfect Inter-Correlations Of Three Types Of Factor Score Predictors, André Beauducel May 2017

A Schmid-Leiman-Based Transformation Resulting In Perfect Inter-Correlations Of Three Types Of Factor Score Predictors, André Beauducel

Journal of Modern Applied Statistical Methods

Factor score predictors are computed when individual factor scores are of interest. Conditions for a perfect inter-correlation of the best linear factor score predictor, the best linear conditionally unbiased predictor, and the determinant best linear correlation-preserving predictor are presented. A transformation resulting in perfect correlations of the three predictors is proposed.

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Errors In A Program For Approximating Confidence Intervals, Andrew V. Frane May 2017

Errors In A Program For Approximating Confidence Intervals, Andrew V. Frane

Journal of Modern Applied Statistical Methods

An SPSS script previously presented in this journal contained nontrivial flaws. The script should not be used as written. A call is renewed for validation of new software.

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An Empirical Comparison Between Robust Estimation And Robust Optimization To Mean-Variance Portfolio, Epha Diana Supandi, Dedi Rosadi, Abdurakhman May 2017

An Empirical Comparison Between Robust Estimation And Robust Optimization To Mean-Variance Portfolio, Epha Diana Supandi, Dedi Rosadi, Abdurakhman

Journal of Modern Applied Statistical Methods

Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Recently, there are two approaches designed to reduce the effect of estimation error: robust statistics and robust optimization. Two different robust portfolios were examined by assessing the out-of-sample performance and the stability of optimal portfolio compositions. The performance of the proposed robust portfolios was compared to classical portfolios via expected return, risk, and Sharpe Ratio. The aim is to shed light on the debate concerning the importance of the estimation error and weights stability in the portfolio allocation problem, and …

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Guidelines For Generating Right-Censored Outcomes From A Cox Model Extended To Accommodate Time-Varying Covariates, Maria E. Montez-Rath, Kristopher Kapphahn, Maya B. Mathur, Aya A. Mitani, David J. Hendry, Manisha Desai May 2017

Guidelines For Generating Right-Censored Outcomes From A Cox Model Extended To Accommodate Time-Varying Covariates, Maria E. Montez-Rath, Kristopher Kapphahn, Maya B. Mathur, Aya A. Mitani, David J. Hendry, Manisha Desai

Journal of Modern Applied Statistical Methods

Simulating studies with right-censored outcomes as functions of time-varying covariates is discussed. Guidelines on the use of an algorithm developed by Zhou and implemented by Hendry are provided. Through simulation studies, the sensitivity of the method to user inputs is considered.

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A Reinterpretation And Extension Of Mcnemar’S Test, Chauncey M. Dayton May 2017

A Reinterpretation And Extension Of Mcnemar’S Test, Chauncey M. Dayton

Journal of Modern Applied Statistical Methods

The McNemar test is extended to multiple groups based on a latent class model incorporating classes representing consistent responders and a single latent error rate. The method is illustrated with data from a CDC survey of immunizations for flu and pneumonia for which a part-heterogeneous model is selected for interpretation.

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In Response To Frane, "Errors In A Program For Approximating Confidence Intervals", David A. Walker May 2017

In Response To Frane, "Errors In A Program For Approximating Confidence Intervals", David A. Walker

Journal of Modern Applied Statistical Methods

A rebuttal to Frane's letter to the Editor in this issue.

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Experiment-Wise Type I Error Rates In Nested (Hierarchical) Study Designs, Jack Sawilowsky, Barry Markman May 2017

Experiment-Wise Type I Error Rates In Nested (Hierarchical) Study Designs, Jack Sawilowsky, Barry Markman

Journal of Modern Applied Statistical Methods

When conducting a statistical test one of the initial risks that must be considered is a Type I error, also known as a false positive. The Type I error rate is set by nominal alpha, assuming all underlying conditions of the statistic are met. Experiment-wise Type I error inflation occurs when multiple tests are conducted overall for a single experiment. There is a growing trend in the social and behavioral sciences utilizing nested designs. A Monte Carlo study was conducted using a two-layer design. Five theoretical distributions and four real datasets taken from Micceri (1989) were used, each with five …

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Control Charts For Mean For Non-Normally Correlated Data, J. R. Singh, Ab Latif Dar May 2017

Control Charts For Mean For Non-Normally Correlated Data, J. R. Singh, Ab Latif Dar

Journal of Modern Applied Statistical Methods

Traditionally, quality control methodology is based on the assumption that serially-generated data are independent and normally distributed. On the basis of these assumptions the operating characteristic (OC) function of the control chart is derived after setting the control limits. But in practice, many of the basic industrial variables do not satisfy both the assumptions and hence one may doubt the validity of the inferences drawn from the control charts. In this paper the power of the control chart for the mean is examined when both the assumptions of independence and normality are not tenable. The OC function is calculated and …

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Multivariate Rank Outlyingness And Correlation Effects, Olusola Samuel Makinde May 2017

Multivariate Rank Outlyingness And Correlation Effects, Olusola Samuel Makinde

Journal of Modern Applied Statistical Methods

The effect of correlation on multivariate rank outlyingness, a result of deviation of multivariate rank functions from property of spherical symmetry, is examined. Possible affine invariant versions of this multivariate rank are surveyed, and outlyingness of affine invariant and non-invariant spatial rank functions under general affine transformation are compared.

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A Comparison Of Different Methods Of Zero-Inflated Data Analysis And An Application In Health Surveys, Si Yang, Lisa L. Harlow, Gavino Puggioni, Colleen A. Redding May 2017

A Comparison Of Different Methods Of Zero-Inflated Data Analysis And An Application In Health Surveys, Si Yang, Lisa L. Harlow, Gavino Puggioni, Colleen A. Redding

Journal of Modern Applied Statistical Methods

The performance of several models under different conditions of zero-inflation and dispersion are evaluated. Results from simulated and real data showed that the zero-altered or zero-inflated negative binomial model were preferred over others (e.g., ordinary least-squares regression with log-transformed outcome, Poisson model) when data have excessive zeros and over-dispersion.

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Test Statistics For The Comparison Of Means For Two Samples That Include Both Paired And Independent Observations, Ben Derrick, Bethan Russ, Deirdre Toher, Paul White May 2017

Test Statistics For The Comparison Of Means For Two Samples That Include Both Paired And Independent Observations, Ben Derrick, Bethan Russ, Deirdre Toher, Paul White

Journal of Modern Applied Statistical Methods

Standard approaches for analyzing the difference in two means, where partially overlapping samples are present, are less than desirable. Here are introduced two test statistics, making reference to the t-distribution. It is shown that these test statistics are Type I error robust, and more powerful than standard tests.

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Graphical Log-Linear Models: Fundamental Concepts And Applications, Niharika Gauraha May 2017

Graphical Log-Linear Models: Fundamental Concepts And Applications, Niharika Gauraha

Journal of Modern Applied Statistical Methods

A comprehensive study of graphical log-linear models for contingency tables is presented. High-dimensional contingency tables arise in many areas. Analysis of contingency tables involving several factors or categorical variables is very hard. To determine interactions among various factors, graphical and decomposable log-linear models are preferred. Connections between the conditional independence in probability and graphs are explored, followed with illustrations to describe how graphical log-linear model are useful to interpret the conditional independences between factors. The problem of estimation and model selection in decomposable models is discussed.

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Robustness And Power Comparison Of The Mood-Westenberg And Siegel-Tukey Tests, Linda C. Lowenstein, Shlomo S. Sawilowsky May 2017

Robustness And Power Comparison Of The Mood-Westenberg And Siegel-Tukey Tests, Linda C. Lowenstein, Shlomo S. Sawilowsky

Journal of Modern Applied Statistical Methods

The Mood-Westenberg and Siegel-Tukey tests were examined to determine their robustness with respect to Type-I error for detecting variance changes when their assumptions of equal means were slightly violated, a condition that approaches the Behrens-Fisher problem. Monte Carlo methods were used via 34,606 variations of sample sizes, α levels, distributions/data sets, treatments modeled as a change in scale, and treatments modeled as a shift in means. The Siegel-Tukey was the more robust, and was able to handle a more diverse set of conditions.

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A New Estimator For The Pickands Dependence Function, Marta Ferreira May 2017

A New Estimator For The Pickands Dependence Function, Marta Ferreira

Journal of Modern Applied Statistical Methods

The Pickands dependence function characterizes an extreme value copula, a useful tool in the modeling of multivariate extremes. A new estimator is presented along with its convergence properties and performance through simulation.

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Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay May 2017

Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay

Journal of Modern Applied Statistical Methods

Estimation of population variance in two-phase (double) sampling is considered using information on multiple auxiliary variables. An unbiased estimator is proposed and its properties are studied under two different structures. The superiority of the suggested estimator over some contemporary estimators of population variance was established through empirical studies from a natural and an artificially generated dataset.

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Vol. 16, No. 1 (Full Issue), Jmasm Editors May 2017

Vol. 16, No. 1 (Full Issue), Jmasm Editors

Journal of Modern Applied Statistical Methods

.

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Understanding Urban Wilds: Nature, Culture, And Management, Ted Randich May 2017

Understanding Urban Wilds: Nature, Culture, And Management, Ted Randich

Sustainability and Social Justice

Undeveloped natural areas in cities, or urban wilds, are an invaluable resource to urban populations. They provide space for physical activity, mental recovery and relaxation, and nature interaction and learning, among other benefits. Through observation, intercept survey, and interviews, this study explores three urban wilds sites in Worcester, Massachusetts. Current literature covers definitions of what makes nature “natural,” contemporary practices in conservation, and evidence of the benefits listed above. This paper builds on the literature, shedding light on what urban wilds are made up of (nature), how they are used (culture), and how they are sustained through management. A conceptual …

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Digital Hegemonies: The Localness Of Search Engine Results, Andrea Ballatore, Mark Graham, Shilad Sen May 2017

Digital Hegemonies: The Localness Of Search Engine Results, Andrea Ballatore, Mark Graham, Shilad Sen

Faculty Publications

Every day, billions of Internet users rely on search engines to find information about places to make decisions about tourism, shopping, and countless other economic activities. In an opaque process, search engines assemble digital content produced in a variety of locations around the world and make it available to large cohorts of consumers. Although these representations of place are increasingly important and consequential, little is known about their characteristics and possible biases. Analyzing a corpus of Google search results generated for 188 capital cities, this article investigates the geographic dimension of search results, focusing on searches such as “Lagos” and …

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Optimized Forecasting Of Dominant U.S. Stock Market Equities Using Univariate And Multivariate Time Series Analysis Methods, Michael Schwartz May 2017

Optimized Forecasting Of Dominant U.S. Stock Market Equities Using Univariate And Multivariate Time Series Analysis Methods, Michael Schwartz

Computational and Data Sciences Theses

This dissertation documents an investigation into forecasting U.S. stock market equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and multivariate structures are applied and results are compared. One objective is to explore the hypothesis that a multivariate model produces superior performance over a univariate configuration. Another objective is to compare the forecasting performance of ARIMA to SSA, as SSA is a relatively recent development …

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Financial Assessment Of Agricultural Lands At Risk To Coastal Salt Marsh Migration In Relation To Climate Change Induced Sea Level Rise In Dorchester County, Maryland, Jewell Porter May 2017

Financial Assessment Of Agricultural Lands At Risk To Coastal Salt Marsh Migration In Relation To Climate Change Induced Sea Level Rise In Dorchester County, Maryland, Jewell Porter

Sustainability and Social Justice

The increasing rate and effects of sea level rise is a major environmental concern in the Chesapeake Bay. This paper evaluates the impacts of rising sea level on coastal salt marshes and the surrounding agricultural lands at risk in Dorchester County, Maryland to build off existing environmental monitoring work performed by NOAA’s Sentinel Site Program. The results of the spatial analysis were used to estimate monetary benefits to incentivize farmers to protect these marshes by making their land available for marsh migration to occur. Looking at three scenarios of sea level rise and marsh migration, grain crops (corn, soybeans, and …

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