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Articles 1 - 3 of 3
Full-Text Articles in Other Statistics and Probability
Imputation Strategies For Different Categories Of Missing Data, Karthik Chalumuri
Imputation Strategies For Different Categories Of Missing Data, Karthik Chalumuri
Honors Theses and Capstones
Addressing missing data in research is crucial for ensuring the reliability and validity of study findings, yet it remains a significant challenge. This study investigates the impact of missing data on research outcomes and explores the underutilization of existing tools for managing missingness, potentially leading to gaps in critical information with tangible implications for decision-making processes (Dziura et al.).
Focusing on the different categories of missing data—Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR)—this research examines various imputation strategies tailored to each category. Specifically, we compare the efficacy of several model-based imputation methods, …
Realtime Event Detection In Sports Sensor Data With Machine Learning, Mallory Cashman
Realtime Event Detection In Sports Sensor Data With Machine Learning, Mallory Cashman
Honors Theses and Capstones
Machine learning models can be trained to classify time series based sports motion data, without reliance on assumptions about the capabilities of the users or sensors. This can be applied to predict the count of occurrences of an event in a time period. The experiment for this research uses lacrosse data, collected in partnership with SPAITR - a UNH undergraduate startup developing motion tracking devices for lacrosse. Decision Tree and Support Vector Machine (SVM) models are trained and perform with high success rates. These models improve upon previous work in human motion event detection and can be used a reference …
Reply To Valverde, Paul B. Thompson
Reply To Valverde, Paul B. Thompson
RISK: Health, Safety & Environment (1990-2002)
Professor Thompson responds to Valverde's argument, in the last issue, that his approach to Risk puts too much emphasis on the distinction between Risk subjectivism and Risk objectivism. In doing so, he asserts, inter alia, that anchoring Risk judgments in a probabilistic framework does not go far enough in rejecting reigning Risk-analysis notions of "real Risk."