Your Career Begins Here
Graduates of the department have a wide range of career prospects, including agricultural research, data analysis, and working for government agencies that oversee agriculture policies and programs.
The efficiency of data collection is crucial in many areas, including agriculture, engineering, and intelligent conversational systems. In this talk, Guo will present her recent work on optimizing the data collection strategy by developing advanced machine-learning techniques. The proposed approach centers around leveraging the power of deep neural networks and maximizing the information gained from data within the framework of Bayesian optimal experimental design (BOED). To measure the information gain, she will introduce an innovative contrastive mutual information (MI) estimator to serve as an information-rich criterion under the BOED framework. This new MI estimator addresses the drawbacks of existing estimators by eliminating the need for explicit probabilistic descriptions of the model or likelihood functions. The performance of the proposed method is evaluated by both numerical examples and real applications.
A multi-armed trial based on ordinal outcomes is proposed that leverages a flexible non-proportional odds cumulative logit model and numerical utility scores for each outcome to determine treatment optimality. This trial design uses a Bayesian clustering prior on the treatment effects that encourages the pairwise null hypothesis of no differences between treatments. A group sequential design is proposed to determine which treatments are clinically different with an adaptive decision boundary that becomes more aggressive as the sample size or clinical significance grows, or the number of active treatments decreases. A simulation study is conducted for three and five treatment arms, which shows that the design has superior operating characteristics (family wise error rate, generalized power, average sample size) compared to utility designs that do not allow clustering, a frequentist proportional odds model, or a permutation test based on empirical mean utilities.
JMP Pro statistics software combines comprehensive statistical capabilities with an interactive, no-code interface. Its ease-of-use makes it a strong teaching tool, and its powerful analysis capabilities have led to adoption across academia and industry. All LSU faculty and students have access to JMP Pro (download on tigerware.lsu.edu).
On Sept. 15, the JMP Academic team will deliver two seminars on JMP Pro for basic-to-advanced data visualization, statistical modeling, and machine learning.
Join the LSU Department of Experimental Statistics to prepare for an exciting career in data analysis. As the primary source of statistical education, research, and service at LSU and the LSU AgCenter, our faculty is focused on providing you with the skills and knowledge you need to thrive in this field.
Our department has a strong orientation towards applied statistics, and we offer both thesis and non-thesis programs leading to a Master of Applied Statistics (M.Ap.Stat.) degree and Ph.D. in Statistics. With a range of specializations, our programs are tailored to help you achieve your unique career goals.
Discover our unparalleled opportunities for research and expert statistical support, and join our community of passionate statisticians today.