Seminars

Wen Zhou, Colorado State: Integrative Group Factor Model for Variable Clustering on Temporally Dependent Date: Optimality and Algorithm

Monday, April 18, 2022 - 11:00am

Presenter: Dr. Wen Zhou, Colorado State University

Time: 11:00 AM Central Time, Monday, April 18, 2022

Title: Integrative Group Factor Model for Variable Clustering on Temporally Dependent Date: Optimality and Algorithm Read more about Wen Zhou, Colorado State: Integrative Group Factor Model for Variable Clustering on Temporally Dependent Date: Optimality and Algorithm

PhD Seminar: Miranda Tilberg, "Confidence intervals for the utilization distribution overlap index (UDOI)"

Tuesday, April 12, 2022 - 3:00pm

Location: Snedecor 1109 (live stream available at https://iastate.zoom.us/j/91269172339?pwd=M2xMSUJxZkxxWm9FcEdhbTNhODJOdz09)

Presenter: Miranda Tilberg, PhD Candidate in Statistics

Title: Confidence intervals for the utilization distribution overlap index (UDOI) Read more about PhD Seminar: Miranda Tilberg, "Confidence intervals for the utilization distribution overlap index (UDOI)"

Leah R. Johnson, Virginia Tech: "Strategic vs Tactical Modeling Approaches to Predicting Mosquito-borne Disease in the Americas"

Monday, April 11, 2022 - 11:00am

Presenter: Dr. Leah R. Johnson, Virginia Tech University

Time: 11:00 AM Central Time, Monday, April 11, 2022

Title: Strategic vs Tactical Modeling Approaches to Predicting Mosquito-borne Disease in the Americas Read more about Leah R. Johnson, Virginia Tech: "Strategic vs Tactical Modeling Approaches to Predicting Mosquito-borne Disease in the Americas"

Lingzhou Xue, Penn State University: An Additive Graphical Model for Discrete Data

Monday, March 21, 2022 - 11:00am

Abstract: We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence. Additive conditional independence is a three-way statistical relation that shares similar properties with conditional independence by satisfying the semi-graphoid axioms. Based on this relation we build an additive graphical model for discrete variables that does not suffer from the restriction of a parametric model such as the Ising model. Read more about Lingzhou Xue, Penn State University: An Additive Graphical Model for Discrete Data

Yuhlong Lio, University of South Dakota: Robust Control Charts for Percentiles Based on Location-Scale Family of Distributions

Monday, March 7, 2022 - 11:00am

Abstract: Robust control charts for percentiles based on location-scale family of distributions are proposed. In the construction of control charts for percentiles, when the underlying distribution of the quality measurement is unknown, we study the problem of discriminating different possible candidate distributions in the location-scale family of distributions and obtain control charts for percentiles which are insensitive to model mis-specification. Read more about Yuhlong Lio, University of South Dakota: Robust Control Charts for Percentiles Based on Location-Scale Family of Distributions

Won Chang, University of Cincinnati: Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

Monday, February 28, 2022 - 11:00am

Abstract: Most organisms exhibit various endogenous oscillating behaviors which provide crucial information as to how the internal biochemical processes are connected and regulated. Understanding the molecular mechanisms behind these oscillators requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating perturbed conditions with higher resolution. Read more about Won Chang, University of Cincinnati: Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

Seminar: Tracy Ke, Harvard

Monday, February 14, 2022 - 11:00am

Abstract: The probabilistic topic model imposes a low-rank structure on the expectation of the corpus matrix. Therefore, singular value decomposition (SVD) is a natural tool of dimension reduction. We propose an SVD-based method for estimating a topic model. Our method constructs an estimate of the topic matrix from only a few leading singular vectors of the corpus matrix, and has a great advantage in memory use and computational cost for large-scale corpora. Read more about Seminar: Tracy Ke, Harvard

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