Statistics and Probability Commons^™

Testing For Dice Control Based On Observations Of The Length Of The Shooter's Hand, Stewart N. Ethier, Hokwon Cho

International Conference on Gambling & Risk Taking

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The Q-Analogue Of The Extended Generalized Gamma Distribution, Wenhao Chen

Undergraduate Student Research Internships Conference

This project introduces a flexible univariate probability model referred to as the q-analogue of the Extended Generalized Gamma (or q-EGG) distribution, which encompasses the majority of the most frequently used continuous distributions, including the gamma, Weibull, logistic, type-1 and type-2 beta, Gaussian, Cauchy, Student-t and F. Closed form representations of its moments and cumulative distribution function are provided. Additionally, computational techniques are proposed for determining estimates of its parameters. Both the method of moments and the maximum likelihood approach are utilized. The effect of each parameter is also graphically illustrated. Certain data sets are modeled with q-EGG distributions; goodness of …

Interpreting Patient Reported Outcomes In Orthopaedic Surgery: A Systematic Review, Shgufta Docter, Zina Fathalla, Michael Lukacs, Michaela Khan, Morgan Jennings, Shu-Hsuan Liu, Dong Zi, Dianne Bryant

Western Research Forum

Background: Reporting methods of patient reported outcome measures (PROMs) vary in orthopaedic surgery literature. While most studies report statistical significance, the interpretation of results would be improved if authors reported confidence intervals (CIs), the minimally clinically important difference (MCID), and number needed to treat (NNT).

Objective: To assess the quality and interpretability of reporting the results of PROMs. To evaluate reporting, we will assess the proportion of studies that reported (1) 95% CIs, (2) MCID, and (3) NNT. To evaluate interpretation, we will assess the proportion of studies that discussed results using the MCID or the effect sizes and how …

Neural Shrubs: Using Neural Networks To Improve Decision Trees, Kyle Caudle, Randy Hoover, Aaron Alphonsus

SDSU Data Science Symposium

Decision trees are a method commonly used in machine learning to either predict a categorical response or a continuous response variable. Once the tree partitions the space, the response is either determined by the majority vote – classification trees, or by averaging the response values – regression trees. This research builds a standard regression tree and then instead of averaging the responses, we train a neural network to determine the response value. We have found that our approach typically increases the predicative capability of the decision tree. We have 2 demonstrations of this approach that we wish to present as …

Bayes Multiple Binary Classifier - How To Make Decisions Like A Bayesian, Wensong Wu

Mathematics Colloquium Series

This presentation will start by a general introduction of Bayesian statistics, which has become popular in the era of big data. Then we consider a two-class classification problem, where the goal is to predict the class membership of M units based on the values of high-dimensional categorical predictor variables as well as both the values of predictor variables and the class membership of other N independent units. We focus on applying generalized linear regression models with Boolean expressions of categorical predictors. We consider a Bayesian and decision-theoretic framework, and develop a general form of Bayes multiple binary classification functions with …