Rachel Gidaro - An Introduction to Estimation and Comparison of Discrete Variate Time Processes

For information on upcoming conferences, visit https://www.rstats.ai. An Introduction to Estimation and Comparison of Discrete Variate Time Processes by Rachel Gidaro Presentation Slides: https://rstatsnyc.blob.core.windows.net/misc/An-Introduction-to-Estimation-and-Comparison-of-Discrete-Variate-Time-Processes_Rachel-Gidaro.pdf Abstract: This talk delves into the parameter estimation of discrete time series, focusing on the comparison between integer-autoregressive processes and traditional Gaussian autoregressive models. We will explore the theoretical underpinnings of these discrete models, emphasizing their relevance in various applications. This talk will largely be an overview of the relevant background in time series analysis, setting the stage for the integer-autoregressive processes. Bio: Dr. Rachel Gidaro is an Assistant Professor in the Department of Mathematical Sciences at the United States Military Academy, West Point, New York. She earned a Bachelor of Arts in Mathematics in 2019 from Colorado Mesa University. Beginning in 2019, she attended Baylor University and earned a Master of Science in Statistics in 2020 before completing her Doctor of Philosophy in Statistics in 2024. Her research focus is discrete variate time series. In her free time, Rachel enjoys reading, exercising, and working with the cheer team, the Rabble Rousers, at USMA. Twitter: https://twitter.com/WestPoint_USMA Presented at the 2024 Government & Public Sector R Conference (October 30, 2024) Hosted by Lander Analytics (https://www.landeranalytics.com)