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Articles 1 - 6 of 6
Full-Text Articles in Categorical Data Analysis
Scalar-On-Function Regression: Estimation And Inference Under Complex Survey Designs, Ekaterina Smirnova, Erjia Cui, Lucia Tabacu, Andrew Leroux
Scalar-On-Function Regression: Estimation And Inference Under Complex Survey Designs, Ekaterina Smirnova, Erjia Cui, Lucia Tabacu, Andrew Leroux
Mathematics & Statistics Faculty Publications
Increasingly, large, nationally representative health and behavioral surveys conducted under a multistage stratified sampling scheme collect high dimensional data with correlation structured along some domain (eg, wearable sensor data measured continuously and correlated over time, imaging data with spatiotemporal correlation) with the goal of associating these data with health outcomes. Analysis of this sort requires novel methodologic work at the intersection of survey statistics and functional data analysis. Here, we address this crucial gap in the literature by proposing an estimation and inferential framework for generalizable scalar-on-function regression models for data collected under a complex survey design. We propose to: …
The Effectiveness Of Visualization Techniques For Supporting Decision-Making, Cansu Yalim, Holly A. H. Handley
The Effectiveness Of Visualization Techniques For Supporting Decision-Making, Cansu Yalim, Holly A. H. Handley
Modeling, Simulation and Visualization Student Capstone Conference
Although visualization is beneficial for evaluating and communicating data, the efficiency of various visualization approaches for different data types is not always evident. This research aims to address this issue by investigating the usefulness of several visualization techniques for various data kinds, including continuous, categorical, and time-series data. The qualitative appraisal of each technique's strengths, weaknesses, and interpretation of the dataset is investigated. The research questions include: which visualization approaches perform best for different data types, and what factors impact their usefulness? The absence of clear directions for both researchers and practitioners on how to identify the most effective visualization …
Carbon Dioxide And Particulate Matter Concentration On Hampton Roads Air Quality, Gregory Hubbard
Carbon Dioxide And Particulate Matter Concentration On Hampton Roads Air Quality, Gregory Hubbard
OUR Journal: ODU Undergraduate Research Journal
Hampton Roads has been a maritime crossroads for the last 400 years. Industrialization has impacted the coastal region for the last 250 years. The expansion of the Port of Virginia in 2019 has created dense traffic in the region resulting in impacts to air quality. Two waste products that affect humans are particulate matter and carbon dioxide. Both respective emissions can cause adverse effects on humans, such as asthma, some lung cancers, and other respiratory distress. Scientists and health practitioners are studying the effects of particulate matter on human health. Hampton Roads, in particular, because of its unique location on …
Responding To Some Challenges Posed By The Re-Identification Of Anonymized Personal Data, Herman T. Tavani, Frances S. Grodzinsky
Responding To Some Challenges Posed By The Re-Identification Of Anonymized Personal Data, Herman T. Tavani, Frances S. Grodzinsky
Computer Ethics - Philosophical Enquiry (CEPE) Proceedings
In this paper, we examine a cluster of ethical controversies generated by the re-identification of anonymized personal data in the context of big data analytics, with particular attention to the implications for personal privacy. Our paper is organized into two main parts. Part One examines some ethical problems involving re-identification of personally identifiable information (PII) in large data sets. Part Two begins with a brief description of Moor and Weckert’s Dynamic Ethics (DE) and Nissenbaum’s Contextual Integrity (CI) Frameworks. We then investigate whether these frameworks, used together, can provide us with a more robust scheme for analyzing privacy concerns that …
Dimensionality And Factorial Invariance Of Religiosity Among Christians And The Religiously Unaffiliated: A Cross-Cultural Analysis Based On The International Social Survey Programme, Carlos Miguel Lemos, Ross Joseph Gore, Ivan Puga-Gonzalez, F. Leron Shults
Dimensionality And Factorial Invariance Of Religiosity Among Christians And The Religiously Unaffiliated: A Cross-Cultural Analysis Based On The International Social Survey Programme, Carlos Miguel Lemos, Ross Joseph Gore, Ivan Puga-Gonzalez, F. Leron Shults
VMASC Publications
We present a study of the dimensionality and factorial invariance of religiosity for 26 countries with a Christian heritage, based on the 1998 and 2008 rounds of the International Social Survey Programme (ISSP) Religion survey, using both exploratory and multi-group confirmatory factor analyses. The results of the exploratory factor analysis showed that three factors, common to Christian and religiously unaffiliated respondents, could be extracted from our initially selected items and suggested the testing of four different three-factor models using multi-group confirmatory factor analysis. For the model with the best fit and measurement invariance properties, we labeled the three resulting factors …
Alternatives To Mixture Model Analysis Of Correlated Binomial Data, N. Rao Chaganty, Roy Sabo, Yihao Deng
Alternatives To Mixture Model Analysis Of Correlated Binomial Data, N. Rao Chaganty, Roy Sabo, Yihao Deng
Mathematics & Statistics Faculty Publications
While univariate instances of binomial data are readily handled with generalized linear models, cases of multivariate or repeated measure binomial data are complicated by the possibility of correlated responses. Likelihood-based estimation can be applied by using mixture distribution models, though this approach can present computational challenges. The logistic transformation can be used to bypass these concerns and allow for alternative estimating procedures. One popular alternative is the generalized estimating equation (GEE) method, though systematic errors can lead to infeasible correlation estimates or nonconvergence problems. Our approach is the coupling of quasileast squares (QLSs) method with a rarely used matrix factorization, …