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Articles 1 - 3 of 3
Full-Text Articles in Statistical Models
Bayesian Structural Time Series Methods For Modeling Cattle Body Temperature In Heat-Stressed Animals, Lacey Quandt
Bayesian Structural Time Series Methods For Modeling Cattle Body Temperature In Heat-Stressed Animals, Lacey Quandt
Murray State Theses and Dissertations
Climate change has had devastating effects globally, most commonly talked about during natural disasters and rising temperatures. Notably, the climate concern is turning towards agriculture and livestock. With rising temperatures, the prolonged amount of heat stress put on animals, specifically cattle, is becoming more apparent. Heat stress has been linked to a reduction in cattle growing and fattening, feed intake, productivity, reproduction, and fertility; increased heart rates and respiration; changes in behavior; and mortality in severe cases. There are abatement strategies put in place to lower heat stress in cattle, such as improvements in shading and cooling, nutritional management, and …
Evaluating An Ordinal Output Using Data Modeling, Algorithmic Modeling, And Numerical Analysis, Martin Keagan Wynne Brown
Evaluating An Ordinal Output Using Data Modeling, Algorithmic Modeling, And Numerical Analysis, Martin Keagan Wynne Brown
Murray State Theses and Dissertations
Data and algorithmic modeling are two different approaches used in predictive analytics. The models discussed from these two approaches include the proportional odds logit model (POLR), the vector generalized linear model (VGLM), the classification and regression tree model (CART), and the random forests model (RF). Patterns in the data were analyzed using trigonometric polynomial approximations and Fast Fourier Transforms. Predictive modeling is used frequently in statistics and data science to find the relationship between the explanatory (input) variables and a response (output) variable. Both approaches prove advantageous in different cases depending on the data set. In our case, the data …
Neural Network Predictions Of A Simulation-Based Statistical And Graph Theoretic Study Of The Board Game Risk, Jacob Munson
Neural Network Predictions Of A Simulation-Based Statistical And Graph Theoretic Study Of The Board Game Risk, Jacob Munson
Murray State Theses and Dissertations
We translate the RISK board into a graph which undergoes updates as the game advances. The dissection of the game into a network model in discrete time is a novel approach to examining RISK. A review of the existing statistical findings of skirmishes in RISK is provided. The graphical changes are accompanied by an examination of the statistical properties of RISK. The game is modeled as a discrete time dynamic network graph, with the various features of the game modeled as properties of the network at a given time. As the network is computationally intensive to implement, results are produced …