Anatomy Of A Conspiracy Theory: Law, Politics, And Science Denialism In The Era Of Covid-19, 2021 Texas Tech University School of Law
Anatomy Of A Conspiracy Theory: Law, Politics, And Science Denialism In The Era Of Covid-19, Brie Sherwin
Texas A&M Law Review
With COVID-19, we are facing the most serious public health threat of our lifetime. Now, more than ever, we need experts and sound scientific advice to guide critical decision-making during the pandemic. With conspiracy theories and other similar rhetorical weapons being used to discredit our scientific experts, we face a myriad of misinformation, mistruths, and all-out attacks on our experts, breeding distrust between the public and the policymakers leading the fight against the pandemic. As President Trump took office, scientists were routinely denigrated and isolated. Furthermore, science denialism has permeated its way up to the highest levels of government, resulting ...
Joint Modeling Of Distances And Times In Point-Count Surveys, 2021 University of Michigan
Joint Modeling Of Distances And Times In Point-Count Surveys, Adam Martin-Schwarze, Jarad Niemi, Philip Dixon
Removal and distance modeling are two common methods to adjust counts for imperfect detection in point-count surveys. Several recent articles have formulated models to combine them into a distance-removal framework. We observe that these models fall into two groups building from different assumptions about the joint distribution of observed distances and first times to detection. One approach assumes the joint distribution results from a Poisson process (PP). The other assumes an independent joint (IJ) distribution with its joint density being the product of its marginal densities. We compose an IJ+PP model that more flexibly models the joint distribution and ...
Management Of Multiple Sources Of Risk In Livestock Production, 2021 Michigan State University
Management Of Multiple Sources Of Risk In Livestock Production, Melissa G. S. Mckendree, Glynn T. Tonsor, Lee L. Schulz
Firm operators continually manage multiple sources of risk. In an application to cattle feedlot operations, our objective is to determine if producers view output price and animal health risks separately or jointly. We conduct a survey with a choice experiment placing operators in forward looking, decision-making scenarios, and capture information on past risk management approaches. Evidence regarding a relationship between animal health and output price risk mitigation is mixed and depends on the decision being made. Combined, these results provide new insight into how managers approach multiple risks when facing resource constraints.
Examining Multiple Imputation For Measurement Error Correction In Count Data With Excess Zeros, 2020 Southern Methodist University
Examining Multiple Imputation For Measurement Error Correction In Count Data With Excess Zeros, Shalima Zalsha
Statistical Science Theses and Dissertations
Measurement error and missing data are two common problems in wildlife population surveys. These data are collected from the environment and may be missing or measured with error when the observer’s ability to see the animal is obscured. Methods such as video transects for estimating red snapper abundance and aerial surveys for estimating moose population sizes are highly affected by these problems since total abundance will be underestimated if missing/mismeasured counts are ignored. We shall refer to this problem as visibility bias; it occurs when the true counts are observed when visibility is high, partially observed when visibility ...
Direct Questioning Of Sensitive Topics In Public Health Studies: A Simulation Study, 2020 LSU Health Sciences Center, School of Public Health, Biostatistics Program
Direct Questioning Of Sensitive Topics In Public Health Studies: A Simulation Study, Jessica K. Fox, Evrim Oral
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Resource-Saving Technologies For The Production Of Elastic Leather Materials: Collective Monograph, 2020 University of Tennessee, Knoxville
Resource-Saving Technologies For The Production Of Elastic Leather Materials: Collective Monograph, Olena Korotych, Anatolii Danylkovych, Serhii Bilinskyi, Serhii Bondarenko, Slava Branovitska, Vasyl Chervinskyi, Nataliia Khliebnikova, Alona Kudzieva, Viktor Lishchuk, Nataliia Lysenko, Olena Mokrousova, Nataliia Omelchenko, Vera Palamar, Yuliia Potakh, Oksana Romanyuk, Olga Sanginova, Oleksandr Zhyhotsky
Chemistry Publications and Other Works
This monograph contains a collection of recent research papers focusing on advancing existing technologies and developing new technologies to improve the environmentally friendliness and save resources during the production of elastic leather materials. The papers are organized based on the type of technological process used to preserve raw hides. A lot of attention is devoted to mathematical planning, simulations, and multicriteria optimization of the technological processes using newly developed chemical reagents. The monograph contains a complex study of physicochemical properties and characteristics of the resulting leather materials. The developed technologies were tested by the private joint-stock company Chinbar (Kyiv, Ukraine ...
Maximum Entropy Classification For Record Linkage, 2020 University of Alabama
Maximum Entropy Classification For Record Linkage, Danhyang Lee, Li-Chun Zhang, Jae Kwang Kim
By record linkage one joins records residing in separate files which are believed to be related to the same entity. In this paper we approach record linkage as a classification problem, and adapt the maximum entropy classification method in text mining to record linkage, both in the supervised and unsupervised settings of machine learning. The set of links will be chosen according to the associated uncertainty. On the one hand, our framework overcomes some persistent theoretical flaws of the classical approach pioneered by Fellegi and Sunter (1969); on the other hand, the proposed algorithm is scalable and fully automatic, unlike ...
Semiparametric Imputation Using Conditional Gaussian Mixture Models Under Item Nonresponse, 2020 University of Alabama
Semiparametric Imputation Using Conditional Gaussian Mixture Models Under Item Nonresponse, Danhyang Lee, Jae Kwang Kim
Imputation is a popular technique for handling item nonresponse in survey sampling. Parametric imputation is based on a parametric model for imputation and is less robust against the failure of the imputation model. Nonparametric imputation is fully robust but is not applicable when the dimension of covariates is large due to the curse of dimensionality. Semiparametric imputation is another robust imputation based on a flexible model where the number of model parameters can increase with the sample size. In this paper, we propose another semiparametric imputation based on a more flexible model assumption than the Gaussian mixture model. In the ...
Rdc Data Alternatives: Conducting Research During Covid-19, 2020 Western University
Rdc Data Alternatives: Conducting Research During Covid-19, Kristi Thompson, Elizabeth Hill
Western Libraries Presentations
Recent physical distancing protocols pertaining to the COVID-19 Pandemic have meant that RDC researchers need to find alternatives ways of carrying out their research. The Real Time Remote Access (RTRA) program offers one alternative way to access confidential Statistics Canada data. Other options include using the Statistics Canada public use files and analyzing data from other sources.
The presenters, data librarians from Western Libraries will discuss the differences between the data that can be accessed through the RTRA the RDC. RTRA data is a very useful option for some types of questions but also has some important limitations. We will ...
Do We Need To Reconsider The Cmam Admission And Discharge Criteria?; An Analysis Of Cmam Data In South Sudan, 2020 Seoul National University
Do We Need To Reconsider The Cmam Admission And Discharge Criteria?; An Analysis Of Cmam Data In South Sudan, Eunyong Ahn, Cyprian Ouma, Mesfin Loha, Asrat Dibaba, Wendy Dyment, Jae Kwang Kim, Nam Seon Beck, Taesung Park
Background: Weight-for-height Z-score (WHZ) and Mid Upper Arm Circumference (MUAC) are both commonly used as acute malnutrition screening criteria. However, there exists disparity between the groups identified as malnourished by them. Thus, here we aim to investigate the clinical features and linkage with chronicity of the acute malnutrition cases identified by either WHZ or MUAC. Besides, there exists evidence indicating that fat restoration is disproportionately rapid compared to that of muscle gain in hospitalized malnourished children but related research at community level is lacking. In this study we suggest proxy measure to inspect body composition restoration responding to malnutrition management ...
Doubly Robust Inference When Combining Probability And Non-Probability Samples With High-Dimensional Data, 2020 North Carolina State University
Doubly Robust Inference When Combining Probability And Non-Probability Samples With High-Dimensional Data, Shu Yang, Jae Kwang Kim, Rui Song
Non-probability samples become increasingly popular in survey statistics but may suffer from selection biases that limit the generalizability of results to the target population. We consider integrating a non-probability sample with a probability sample which provides high-dimensional representative covariate information of the target population. We propose a two-step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded-concave penalties to select important variables for the sampling score of selection into the non-probability sample and the outcome model. We show that the penalized estimating equation approach enjoys the selection consistency property for ...
Characterizing Uncertainty In Correlated Response Variables For Pareto Front Optimization, 2020 Air Force Institute of Technology
Characterizing Uncertainty In Correlated Response Variables For Pareto Front Optimization, Peter A. Calhoun
Theses and Dissertations
Current research provides a method to incorporate uncertainty into Pareto front optimization by simulating additional response surface model parameters according to a Multivariate Normal Distribution (MVN). This research shows that analogous to the univariate case, the MVN understates uncertainty, leading to overconfident conclusions when variance is not known and there are few observations (less than 25-30 per response). This research builds upon current methods using simulated response surface model parameters that are distributed according to an Multivariate t-Distribution (MVT), which can be shown to produce a more accurate inference when variance is not known. The MVT better addresses uncertainty in ...
Combining Non-Probability And Probability Survey Samples Through Mass Imputation, 2020 Iowa State University
Combining Non-Probability And Probability Survey Samples Through Mass Imputation, Jae Kwang Kim, Seho Park, Yulin Chen, Changbao Wu
This paper presents theoretical results on combining non-probability and probability survey samples through mass imputation, an approach originally proposed by Rivers (2007) as sample matching without rigorous theoretical justification. Under suitable regularity conditions, we establish the consistency of the mass imputation estimator and derive its asymptotic variance formula. Variance estimators are developed using either linearization or bootstrap. Finite sample performances of the mass imputation estimator are investigated through simulation studies and an application to analyzing a non-probability sample collected by the Pew Research Centre.
Does Water Boil Faster With Salt?, 2020 Misericordia University
Does Water Boil Faster With Salt?, Soumyadip Acharyya
Student Research Poster Presentations 2020
Whether water boils faster with salt is perhaps a never-ending question. My study has addressed this topic from a statistical perspective. Additionally, I have also investigated whether the water quantity affects the boiling time. I used the two-way Analysis of variance (ANOVA) to analyze and interpret the data.
The Prevalent Misuse Of Fisher’S Partial Eta Squared Formula, 2020 University of Iowa
The Prevalent Misuse Of Fisher’S Partial Eta Squared Formula, Mariah Cooper
Honors Theses at the University of Iowa
The recording of an estimate of effect size is an essential tool for empirical science because it allows for statistical power. In addition, it enables researchers to replicate studies because it assists in choosing subject amounts effectively. A popular measure of effect size is partial eta squared and is often calculated using Fisher's formula. Despite the positive impact that partial eta provides to empirical researchers, it comes with two problems. One is that researchers are misusing this formula because it was initially made for between-subject designs. When measuring the effect size via partial eta squared in a between-subject design ...
Assessing Robustness Of The Rasch Mixture Model To Detect Differential Item Functioning - A Monte Carlo Simulation Study, Jinjin Huang
Electronic Theses and Dissertations
Measurement invariance is crucial for an effective and valid measure of a construct. Invariance holds when the latent trait varies consistently across subgroups; in other words, the mean differences among subgroups are only due to true latent ability differences. Differential item functioning (DIF) occurs when measurement invariance is violated. There are two kinds of traditional tools for DIF detection: non-parametric methods and parametric methods. Mantel Haenszel (MH), SIBTEST, and standardization are examples of non-parametric DIF detection methods. The majority of parametric DIF detection methods are item response theory (IRT) based. Both non-parametric methods and parametric methods compare differences among subgroups ...
Reporting And Analysis Of Split Plot Designs In Preclinical Animal Experiments, 2020 Iowa State University
Reporting And Analysis Of Split Plot Designs In Preclinical Animal Experiments, Pu Liu
The split plot design (SPD) has at least two types of experimental units and at least two levels of complete random design. As a result of this SPD structure, a method of analysis that accounts for the different levels of experimental unit is required, which is commonly a mixed model or a split-plot ANOVA. The design is utilized when it is not feasible to randomize the multiple interventions to the same level. The classic example of a split plot arises from agronomy, and gives name to the design, where the effects of two irrigation methods (factor 1) that must be ...
Bread Dough Experiment, 2020 Misericordia University
Bread Dough Experiment, Collin Stivala
Student Research Poster Presentations 2020
This is my Final Poster for Design of Experiments. My poster explains the process and results of my experiment, in which I made bread dough, and tested the effects that Flour and Temperature have on bread dough.
The Effect Of The Amount Of Water And Water Exposure Time On The Absorbency Of Sponges, 2020 Misericordia University
The Effect Of The Amount Of Water And Water Exposure Time On The Absorbency Of Sponges, Danielle Clifford
Student Research Poster Presentations 2020
Given the current global pandemic, now more than ever it is important to understand what factors lead to the best absorbency in a sponge as to stop the spread of bacteria and germs. The purpose of the experiment will be to determine the effect of the amount of time (15 seconds, 30 seconds, 45 seconds, 60 seconds, 75 seconds, and 90 seconds) and the amount of water (24 ounces, 32 ounces, and 40 ounces) on the absorbency of a sponge.
Variance Estimation After Kernel Ridge Regression Imputation, 2020 Iowa State University
Variance Estimation After Kernel Ridge Regression Imputation, Hengfang Wang, Jae Kwang Kim
Statistics Conference Proceedings, Presentations and Posters
Imputation is a popular technique for handling missing data. Variance estimation after imputation is an important practical problem in statistics. In this paper, we consider variance estimation of the imputed mean estimator under the kernel ridge regression imputation. We consider a linearization approach which employs the covariate balancing idea to estimate the inverse of propensity scores. The statistical guarantee of our proposed variance estimation is studied when a Sobolev space is utilized to do the imputation, where n-consistency can be obtained. Synthetic data experiments are presented to conﬁrm our theory.