Valparaiso University
Florida International University
University of Nebraska-Lincoln
Duzce University, Duzce, Turkey
Brigham Young University - Provo
Decision Making In A Changing Environment,
2018
University of Dayton
Decision Making In A Changing Environment, Alan Veliz-Cuba
Annual Symposium on Biomathematics and Ecology: Education and Research
No abstract provided.
Snakebite Dynamics Of Colombia: Effects Of Precipitation Seasonality Of Incidence,
2018
Illinois State University
Snakebite Dynamics Of Colombia: Effects Of Precipitation Seasonality Of Incidence, Carlos Cruz
Annual Symposium on Biomathematics and Ecology: Education and Research
No abstract provided.
Understanding Sexual Violence Against Women,
2018
Illinois State University
Understanding Sexual Violence Against Women, Maria Martinez
Annual Symposium on Biomathematics and Ecology: Education and Research
No abstract provided.
Selected Adventures In Policy Modeling,
2018
Illinois State University
Selected Adventures In Policy Modeling, Edward Kaplan
Annual Symposium on Biomathematics and Ecology: Education and Research
No abstract provided.
Identifying Treatment Effects In The Presence Of Confounded Types,
2018
Iowa State University
Identifying Treatment Effects In The Presence Of Confounded Types, Desire Kedagni
Economics Working Papers
In this paper, I consider identification of treatment effects when
the treatment is endogenous. The use of instrumental variables is a popular
solution to deal with endogeneity, but this may give misleading answers when
the instrument is invalid. I show that when the instrument is invalid due to
correlation with the first stage unobserved heterogeneity, a second (also
possibly invalid) instrument allows to partially identify not only the local
average treatment effect but also the entire potential outcomes distributions
for compliers. I exploit the fact that the distribution of the observed
outcome in each group defined by the treatment and ...
Dealing With Sensitive Quantitative Variables: A Comparison Of Sampling Designs For The Procedure Of Gupta And Thornton,
2018
University of Havana
Dealing With Sensitive Quantitative Variables: A Comparison Of Sampling Designs For The Procedure Of Gupta And Thornton, Carlos Narciso Bouza Herrera, Prayas Sharma
Journal of Modern Applied Statistical Methods
The use of randomized response procedures allows diminishing the number of non-responses and increasing the accuracy of the responses. A new sampling strategy is developed where the reports are scrambled using the procedure of Gupta and Thornton. The estimator of the mean as well as the errors are developed for the Rao-Hartley-Cochran and Ranked Sets Sampling designs. The proposals are compared with the original model based on the use of simple random sampling.
Cross-Sectional Hiv Incidence Estimation Accounting For Heterogeneity Across Communities,
2018
University of Cambridge
Cross-Sectional Hiv Incidence Estimation Accounting For Heterogeneity Across Communities, Yuejia Xu, Oliver B. Laeyendecker, Rui Wang
Harvard University Biostatistics Working Paper Series
No abstract provided.
Comparison Of Multiple Imputation Methods For Categorical Survey Items With High Missing Rates: Application To The Family Life, Activity, Sun, Health And Eating (Flashe) Study,
2018
National Cancer Institute
Comparison Of Multiple Imputation Methods For Categorical Survey Items With High Missing Rates: Application To The Family Life, Activity, Sun, Health And Eating (Flashe) Study, Benmei Liu, Erin Hennessy, April Oh, Laura A. Dwyer, Linda Nebeling
Journal of Modern Applied Statistical Methods
Two multiple imputation methods, the Sequential Regression Multivariate Imputation Algorithm and the Cox-Lannacchione Weighted Sequential Hotdeck, were examined and compared to impute highly missing categorical variables from the Family Life, Activity, Sun, Health and Eating (FLASHE) study. This paper describes the imputation approaches and results from the study.
Bayesian And Semi-Bayesian Estimation Of The Parameters Of Generalized Inverse Weibull Distribution,
2018
Panjab University, Chandigarh, India
Bayesian And Semi-Bayesian Estimation Of The Parameters Of Generalized Inverse Weibull Distribution, Kamaljit Kaur, Kalpana K. Mahajan, Sangeeta Arora
Journal of Modern Applied Statistical Methods
Bayesian and semi-Bayesian estimators of parameters of the generalized inverse Weibull distribution are obtained using Jeffreys’ prior and informative prior under specific assumptions of loss function. Using simulation, the relative efficiency of the proposed estimators is obtained under different set-ups. A real life example is also given.
Overcoming Small Data Limitations In Heart Disease Prediction By Using Surrogate Data,
2018
Southern Methodist University
Overcoming Small Data Limitations In Heart Disease Prediction By Using Surrogate Data, Alfeo Sabay, Laurie Harris, Vivek Bejugama, Karen Jaceldo-Siegl
SMU Data Science Review
In this paper, we present a heart disease prediction use case showing how synthetic data can be used to address privacy concerns and overcome constraints inherent in small medical research data sets. While advanced machine learning algorithms, such as neural networks models, can be implemented to improve prediction accuracy, these require very large data sets which are often not available in medical or clinical research. We examine the use of surrogate data sets comprised of synthetic observations for modeling heart disease prediction. We generate surrogate data, based on the characteristics of original observations, and compare prediction accuracy results achieved from ...
Random Forest Vs Logistic Regression: Binary Classification For Heterogeneous Datasets,
2018
Southern Methodist University
Random Forest Vs Logistic Regression: Binary Classification For Heterogeneous Datasets, Kaitlin Kirasich, Trace Smith, Bivin Sadler
SMU Data Science Review
Selecting a learning algorithm to implement for a particular application on the basis of performance still remains an ad-hoc process using fundamental benchmarks such as evaluating a classifier’s overall loss function and misclassification metrics. In this paper we address the difficulty of model selection by evaluating the overall classification performance between random forest and logistic regression for datasets comprised of various underlying structures: (1) increasing the variance in the explanatory and noise variables, (2) increasing the number of noise variables, (3) increasing the number of explanatory variables, (4) increasing the number of observations. We developed a model evaluation tool ...
Predicting National Basketball Association Success: A Machine Learning Approach,
2018
Southern Methodist University
Predicting National Basketball Association Success: A Machine Learning Approach, Adarsh Kannan, Brian Kolovich, Brandon Lawrence, Sohail Rafiqi
SMU Data Science Review
In this paper, we present a machine learning based approach to projecting the success of National Basketball Association (NBA) draft prospects. With the proliferation of data, analytics have increasingly be- come a critical component in the assessment of professional and collegiate basketball players. We leverage player biometric data, college statistics, draft selection order, and positional breakdown as modelling features in our prediction algorithms. We found that a player's draft pick and their college statistics are the best predictors of their longevity in the National Basketball Association.
Minimizing The Perceived Financial Burden Due To Cancer,
2018
Southern Methodist University
Minimizing The Perceived Financial Burden Due To Cancer, Hassan Azhar, Zoheb Allam, Gino Varghese, Daniel W. Engels, Sajiny John
SMU Data Science Review
In this paper, we present a regression model that predicts perceived financial burden that a cancer patient experiences in the treatment and management of the disease. Cancer patients do not fully understand the burden associated with the cost of cancer, and their lack of understanding can increase the difficulties associated with living with the disease, in particular coping with the cost. The relationship between demographic characteristics and financial burden were examined in order to better understand the characteristics of a cancer patient and their burden, while all subsets regression was used to determine the best predictors of financial burden. Age ...
Yelp’S Review Filtering Algorithm,
2018
Southern Methodist University
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
SMU Data Science Review
In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that ...
Cryptocurrency Price Prediction Using Tweet Volumes And Sentiment Analysis,
2018
Southern Methodist University
Cryptocurrency Price Prediction Using Tweet Volumes And Sentiment Analysis, Jethin Abraham, Daniel Higdon, John Nelson, Juan Ibarra
SMU Data Science Review
In this paper, we present a method for predicting changes in Bitcoin and Ethereum prices utilizing Twitter data and Google Trends data. Bitcoin and Ethereum, the two largest cryptocurrencies in terms of market capitalization represent over \$160 billion dollars in combined value. However, both Bitcoin and Ethereum have experienced significant price swings on both daily and long term valuations. Twitter is increasingly used as a news source influencing purchase decisions by informing users of the currency and its increasing popularity. As a result, quickly understanding the impact of tweets on price direction can provide a purchasing and selling advantage to ...
Coastal Wetland Dynamics Under Sea-Level Rise And Wetland Restoration In The Northern Gulf Of Mexico Using Bayesian Multilevel Models And A Web Tool,
2018
The University of Southern Mississippi
Coastal Wetland Dynamics Under Sea-Level Rise And Wetland Restoration In The Northern Gulf Of Mexico Using Bayesian Multilevel Models And A Web Tool, Tyler Hardy
Master's Theses
There is currently a lack of modeling framework to predict how relative sea-level rise (SLR), combined with restoration activities, affects landscapes of coastal wetlands with uncertainties accounted for at the entire northern Gulf of Mexico (NGOM). I developed such a modeling framework – Bayesian multi-level models to study the spatial pattern of wetland loss in the NGOM, driven by relative RSLR, vegetation productivity, tidal range, coastal slope, and wave height – all interacting with river-borne sediment availability, indicated by hydrological regimes. These interactions have not been comprehensively investigated before. I further modified this model to assess the efficacy of restoration projects from ...
Of Typicality And Predictive Distributions In Discriminant Function Analysis,
2018
Department of Anthropology, University of Illinois at Urbana–Champaign
Of Typicality And Predictive Distributions In Discriminant Function Analysis, Lyle W. Konigsberg, Susan R. Frankenberg
Human Biology Open Access Pre-Prints
While discriminant function analysis is an inherently Bayesian method, researchers attempting to estimate ancestry in human skeletal samples often follow discriminant function analysis with the calculation of frequentist-based typicalities for assigning group membership. Such an approach is problematic in that it fails to account for admixture and for variation in why individuals may be classified as outliers, or non-members of particular groups. This paper presents an argument and methodology for employing a fully Bayesian approach in discriminant function analysis applied to cases of ancestry estimation. The approach requires adding the calculation, or estimation, of predictive distributions as the final step ...
A Math Research Project Inspired By Twin Motherhood,
2018
Valparaiso University
A Math Research Project Inspired By Twin Motherhood, Tiffany N. Kolba
Tiffany N Kolba
The phenomenon of twins, triplets, quadruplets, and other higher order multiples has fascinated humans for centuries and has even captured the attention of mathematicians who have sought to model the probabilities of multiple births. However, there has not been extensive research into the phenomenon of polyovulation, which is one of the biological mechanisms that produces multiple births. In this paper, I describe how my own experience becoming a mother to twins led me on a quest to better understand the scientific processes going on inside my own body and motivated me to conduct research on polyovulation frequencies. An overview of ...
Secondary Data Analysis Project,
2018
Embry-Riddle Aeronautical University
Secondary Data Analysis Project, Jonathan M. Gallimore
SF 420 PR - Gallimore - Fall 2018
This activity is designed to give students an opportunity to apply what they have learned in statistics to a real dataset.
This activity will help students apply what they have learned in statistics to real world data and answer their own research questions. Students will also practice reporting their results in a paper using APA format.
Quantitative Jeopardy Feud,
2018
Embry-Riddle Aeronautical University
Quantitative Jeopardy Feud, Jonathan M. Gallimore
MSF 600 PR - Gallimore - Fall 2018
This activity - Quantitative Jeopardy Feud - is a method for using a game as a final exam.
