Location: Haines Hall A25

Peng Ding, Associate Professor
Department of Statistics, UC Berkeley

Abstract:

Investigating interference or spillover effects among units is a central task in many social science problems. Network experiments are powerful tools for this task, which avoids endogeneity by randomly assigning treatments to units over networks. However, it is non-trivial to analyze network experiments properly without imposing strong modeling assumptions. Previously, many researchers have proposed sophisticated point estimators and standard errors for causal effects under network experiments. We further show that regression-based point estimators and standard errors can have strong theoretical guarantees if the regression functions and robust standard errors are carefully specified to accommodate the interference patterns under network experiments. We first recall a well-known result that the Hajek estimator is numerically identical to the coefficient from the weighted-least-squares fit based on the inverse probability of the exposure mapping. Moreover, we demonstrate that the regression-based approach offers three notable advantages: its ease of implementation, the ability to derive standard errors through the same weighted-least-squares fit, and the capacity to integrate covariates into the analysis, thereby enhancing estimation efficiency. Furthermore, we analyze the asymptotic bias of the regression-based network-robust standard errors. Recognizing that the covariance estimator can be anti-conservative, we propose an adjusted covariance estimator to improve the empirical coverage rates. Although we focus on regression-based point estimators and standard errors, our theory holds under the design-based framework, which assumes that the randomness comes solely from the design of network experiments and allows for arbitrary misspecification of the regression models.

Biography:

Peng Ding is an Associate Professor in the Department of Statistics at UC Berkeley. He obtained his Ph.D. from the Department of Statistics, Harvard University in May 2015, and worked as a postdoctoral researcher in the Department of Epidemiology, Harvard T. H. Chan School of Public Health until December 2015.

Location: Haines Hall A25

Boris Baeumer, Professor
Department of Mathematics and Statistics, University of Otago

Abstract:

In order to capture the increased frequency of earth quakes (aftershocks) after a large event we use a Hawkes process model based on the first relaxation eigenmode of a visco-elastic plate model; i.e. we assume the kernel functions of the Hawkes model are Mittag-Leffler functions. Assuming magnitude and frequency being separable leads to a model that for most data bases outperforms Ogata’s ETAS model predicting earthquake frequency. Removing the restrictive assumption that magnitude and frequency are separable, in order to obtain a parsimonious model of the joint process we need to model the impact of an earthquake of a given magnitude on the intensity measures of all earthquakes. We use marked multivariate Hawkes processes to inform the shape of a parsimonious kernel.

Biography:

Boris Baeumer is a Professor in the Department of Mathematics and Statistics at University of Otago, New Zealand. He obtained his PhD in Mathematics from Louisiana State University. His research interests include non-local PDEs and associated stochastic processes. Over his career he obtained several major research grants and is currently (co-)principal investigator researching “Modelling the domino effect in complex systems”.

Location: Haines Hall A25

Hongzhe Li, Professor
Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania

Abstract:

Population-level single-cell gene expression data captures the gene expressions of thousands of cells from each individual within a sizable cohort. This data enables the construction of cell-type- and individual-specific gene co-expression networks by estimating the covariance matrices. Understanding how such co-expression networks are associated with individual-level covariates is crucial. This paper considers Fréchet regression with the covariance matrix as the outcome and vector covariates, using the Wasserstein distance between covariance matrices as a substitute for the Euclidean distance. A test statistic is defined based on the Fréchet mean and covariate-weighted Fréchet mean. The asymptotic distribution of the test statistic is derived under the assumption of simultaneously diagonalizable covariance matrices. Results from an analysis of large-scale single-cell data reveal an association between the co-expression network of genes in the nutrient sensing pathway and age, indicating a perturbation in gene co-expression networks with aging. More general Fréchet regression on the Bures-Wasserstein manifold will also be discussed and applied to the same single-cell RNA-seq data.

Biography:

Dr. Hongzhe Li is Perelman Professor of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine at the University of Pennsylvania. He is Vice Chair for Research Integration, Director of Center of Statistics in Big Data and former Chair of the Graduate Program in Biostatistics at Penn. He is also a Professor of Statistics and Data Science at the Wharton School and a Professor of Applied Mathematics and Computational Science at Penn. Dr. Li has been elected as a Fellow of the American Statistical Association (ASA), a Fellow of the Institute of Mathematical Statistics (IMS) and a Fellow of American Association for the Advancement of Science (AAAS). Dr. Li served on the Board of Scientific Counselors of the National Cancer Institute of NIH and regularly serves on various NIH study sections. He served as Chair of the Section on Statistics in Genomics and Genetics of the ASA and Co-Editor-in-Chief of Statistics in Biosciences. Dr. Li’s research focuses on developing statistical and computational methods for analysis of large-scale genetic, genomics and metagenomics data and theory on high dimensional statistics. He has trained over 50 PhD students and postdoctoral fellows.