Tags: ASA GA Lectures


Richard A. Davis

Sample Splitting for Assessing Goodness of Fit in Time Series Abstract: A fundamental and often final step in time series modeling is to assess the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are inherently…


Vladimir Dragalin

SIMULATION GUIDED CLINICAL TRIAL DESIGN   Abstract: Simulation guided Clinical Trial Design is a framework for stimulating scientific dialogue between trial designer and clinical team with the final goal of developing the most appropriate design for the study. We create different “what-if” scenarios under different assumptions about the drug effect and simulate data from such scenarios and apply the design. We then see if under a scenario…


Vijay Nair

Presentation Slide - Machine Learning: Overview and Applications Supervised Machine Learning: Applications, Opportunities, and Challenges Abstract Machine learning (ML) algorithms have become popular in business, industry, and technology over the last few decades. This presentation will provide an overview of the developments and applications, with a focus on supervised learning methods that are applied in finance and banking. We will then…


Sujit Ghosh

Possible Hazards of Some Popular Hazard Rate Models Abstract The Cox proportional hazard (PH) model is widely used to determine the effects of risk factors and treatments on survival time of subjects that might be right censored. The selection of covariates depends crucially on the specific form of the conditional hazard model, which is often assumed to be PH, accelerated failure time (AFT), or proportional odds (PO). However, it is shown that…


Sharon Lohr

Please contact Abhyuday Mandal via email at abhyuday@uga.edu to request the zoom link. 


Jeffrey Rosenthal

Link to Lecture


Robust Experimental Designs for Model Calibration

Robust Experimental Designs for Model Calibration A computer model can be used for predicting an output only after specifying the values of some unknown physical constants known as calibration parameters. The unknown calibration parameters can be estimated from real data by conducting physical experiments. This talk presents an approach to optimally design such a physical experiment. The problem of optimally designing physical experiment, using…


Veridical Data Science

Veridical Data Science Veridical data science extracts reliable and reproducible information from data, with an enriched technical language to communicate and evaluate empirical evidence in the context of human decisions and domain knowledge.  Building and expanding on principles of statistics, machine learning, and the sciences, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our…
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