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Full-Text Articles in Physical Sciences and Mathematics
Contrasting Cumulative Risk And Multiple Individual Risk Models Of The Relationship Between Adverse Childhood Experiences (Aces) And Adult Health Outcomes, Marianna Lanoue, Brandon George, Deborah L Helitzer, Scott W Keith
Contrasting Cumulative Risk And Multiple Individual Risk Models Of The Relationship Between Adverse Childhood Experiences (Aces) And Adult Health Outcomes, Marianna Lanoue, Brandon George, Deborah L Helitzer, Scott W Keith
College of Population Health Faculty Papers
BACKGROUND: A very large body of research documents relationships between self-reported Adverse Childhood Experiences (srACEs) and adult health outcomes. Despite multiple assessment tools that use the same or similar questions, there is a great deal of inconsistency in the operationalization of self-reported childhood adversity for use as a predictor variable. Alternative conceptual models are rarely used and very limited evidence directly contrasts conceptual models to each other. Also, while a cumulative numeric 'ACE Score' is normative, there are differences in the way it is calculated and used in statistical models. We investigated differences in model fit and performance between the …
Joint Models Of Longitudinal Outcomes And Informative Time, Jangdong Seo
Joint Models Of Longitudinal Outcomes And Informative Time, Jangdong Seo
Journal of Modern Applied Statistical Methods
Longitudinal data analyses commonly assume that time intervals are predetermined and have no information regarding the outcomes. However, there might be irregular time intervals and informative time. Presented are joint models and asymptotic behaviors of the parameter estimates. Also, the models are applied for real data sets.
Forecasting Daily Stock Market Return With Multiple Linear Regression, Shengxuan Chen
Forecasting Daily Stock Market Return With Multiple Linear Regression, Shengxuan Chen
Mathematics Senior Capstone Papers
The purpose of this project is to use data mining and big data analytic techniques to forecast daily stock market return with multiple linear regression. Using mathematical and statistical models to analyze the stock market is important and challenging. The accuracy of the final results relies on the quality of the input data and the validity of the methodology. In the report, within 5-year period, the data regarding eleven financial and economical features are observed and recorded on each trading day. After preprocessing the raw data with statistical method, we use the multiple linear regression to predict the daily return …