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Dr. Jesus Arroyo Relion

Dr. Jesus Arroyo Relion, Assistant Professor, Department of Statistics, Texas A&M University, presents Simultaneous Prediction and Community Detection for Networks

Date: Friday, April 21, 2023

Community detection is the problem of clustering the vertices of a graph into groups with similar connectivity patterns. Community structure in networks is observed in many different domains, and unsupervised community detection has received a lot of attention in the literature. In neuroimaging applications, community structure is well known to be a feature of brain networks, typically corresponding to different regions of the brain responsible for different functions. However, when the goal is

Dr. Xin Zhang

Dr. Xin (Henry) Zhang, Associate Professor, Department of Statistics, Florida State University, presents Tensor Modeling in Categorical Data Analysis and Association

Date: Friday, April 14, 2023

We introduce new tensor modeling approaches for two multivariate analysis problems. First, we consider the regression of multiple categorical response variables on a high-dimensional predictor. We propose a simple and intuitive latent variable method that incorporates regularization on tensor-formulated parameters. Next, we explore the three-way associations of how two sets of variables associate and interact, given another set of variables. We develop a population dimension reduction model and

Dr. Ben Seiyon Lee

Dr. Ben Seiyon Lee, Assistant Professor, Department of Statistics, George Mason University presents A Distributed Particle-based Approach to Calibrate a Hydrological Model and Assess Flooding Risks

Date: Friday, April 7, 2023

Floods drive dynamic and deeply uncertain risks for people and infrastructures. Hydrologic models provide projections of flood events; however, these deterministic computer models rely on highly uncertain input parameters, which can propagate into uncertain streamflow projections. Uncertainty characterization is a crucial step in improving the predictive understanding of multi-sector dynamics and the design of risk-management strategies. Current approaches to estimate flood hazards often sample

Dr. Rahul Ghosal

Dr. Rahul Ghosal, Department of Epidemiology and Biostatistics, University of South Carolina, presents Shape-Constrained Estimation in Functional Regression with Bernstein Polynomials

Date: Friday, March 24, 2023

Shape restrictions on functional regression coefficients such as non-negativity, monotonicity, convexity or concavity are often available in the form of a prior knowledge or required to maintain a structural consistency in functional regression models. A new estimation method is developed in shape-constrained functional regression models using Bernstein polynomials. Specifically, estimation approaches from nonparametric regression are extended to functional data, properly accounting for shape-co

Dr. Qing Mai

Dr. Qing Mai, Associate Professor, Department of Statistics, Florida State University presents High-dimensional T-distributed Tensor Response Regression

Date: Friday, February 24, 2023

In recent years, promising statistical modeling approaches to tensor data analysis have been rapidly developed. Equipped with tensor algebra and high-dimensional computation techniques, concise and interpretable statistical models and estimation procedures prevail in various applications. One of the biggest challenges to statistical tensor models is the non-Gaussian nature of many real-world data. Unfortunately, existing approaches are either restricted to normality or implicitly using least-sq

Dr. Fui Swen Kuh

Dr. Fui Swen Kuh, Research Fellow, Monash University, Australia presents Using Leave-One-Out Cross-Validation in a Multilevel Regression and Poststratification Workflow: A Cautionary Tale

Date: Monday, February 20, 2023

In recent decades, multilevel regression and poststratification (MRP) has surged in popularity for population inference. However, the validity of the estimates can depend on details of the model, and there is currently little research on validation. We explore how two approximate calculations of leave-one-out cross-validation (LOO) — the Pareto smoothed importance sampling (PSIS-LOO) and a survey-weighted alternative (WTD-PSIS-LOO) can be used to compare Bayesian models for MRP.

Dr. John Stufken

Dr. John Stufken, Professor, Department of Statistics at George Mason University presents Musings on Subdata Selection

Date: Friday, February 10, 2023

Data reduction or summarization methods for large datasets (full data) aim at making inferences by replacing the full data by the reduced or summarized data. Data storage and computational costs are among the primary motivations for this. In this presentation, data reduction will mean the selection of a subset (subdata) of the observations in the full data. While data reduction has been around for decades, its impact continues to grow with approximately 2.5 exabytes (2.5 x 10^18 bytes) of data

Cheng Ly

Dr. Cheng Ly, Associate Professor, Department of Statistical Sciences and Operations Research, VCU presents Variable Neuronal Spiking in a Different Way

Date: Friday, November 4, 2022

At the onset of sensory stimulation, the variability and co-variability of spiking activity is widely reported to decrease, especially in cortex. Considering the potential benefits of such decreased variability for coding, it has been suggested that this could be a general principle governing all sensory systems. We show this is not so. In rats we found increased variability of spiking with odor stimulation. How does this happen?

Subhash Jaini

Subhash Jaini, Managing Director, Keiter presents Oh the Places You'll Go: A Tale of a Data Scientist

Date: Friday, September 23, 2022

Subhash Jaini, managing director at Keiter, will discuss how he got into data science, and what real life use cases for data science he has encountered in different fields of business.

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