11/2Statistics Colloquium, Lucy Gao
Statistics Colloquium, Lucy GaoWednesday, November 2nd, 202204:00 PM - 05:00 PMStorrs CampusVirtual Event
University of British Columbia
Department of Statistics
University of British Columbia
Valid inference after clustering, with application to single-cell
Testing for a difference in means between two groups is fundamental to answering research questions across virtually every scientific area. Standard hypothesis tests (e.g. the t-test) control the type I error rate when the groups to be tested are defined before looking at the data. However, if the groups are instead defined by applying a clustering algorithm to the data, then applying a standard test for a difference in group means to that same data yields an extremely inflated selective type I error rate. This two-step "double-dipping" procedure is common in the analysis of single-cell RNA-sequencing data.
In my talk, I will apply ideas from selective inference to enable valid inference after hierarchical clustering. If time permits, I will also introduce count splitting: a flexible framework that enables valid inference after latent variable estimation in count-valued data, for virtually any latent variable estimation technique and inference approach.
This talk is based on joint work with Jacob Bien (University of Southern California), Daniela Witten and Anna Neufeld (University of Washington), as well as Alexis Battle and Joshua Popp (Johns Hopkins University).
Bio: Lucy is an assistant professor in the Department of Statistics at the University of British Columbia. Prior to UBC, she was an assistant professor at the University of Waterloo. .
Wednesday, November 2, 2022
4:00 pm ET, 1-hour duration
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11/9Statistics Colloquium, Haben Michael
Statistics Colloquium, Haben MichaelWednesday, November 9th, 202204:00 PM - 05:00 PMStorrs CampusVirtual Event
Department of Mathematics and Statistics
University of Massachusetts Amherst
The Population and Personalized AUCs
We consider two generalizations of the area under the curve (AUC) to accommodate clustered data. We describe situations in which the two cluster AUCs diverge and other situations in which they coincide. We apply the results to data collected on urban policing behavior. Pre-print.
DATE: Wednesday, November 9, 2022
TIME: 4:00 p.m.
PLACE: Philip E. Austin Bldg (AUST) room 434
Coffee at 3:30, AUST 326
Pizza after colloquium, AUST 326 (rsvp email@example.com)
RMME-STAT ColloquiumFriday, November 11th, 202211:00 AM - 12:00 PMStorrs CampusVirtual Event
11/18Statistics Colloquium, Hongtu Zhu
Statistics Colloquium, Hongtu ZhuFriday, November 18th, 202210:00 AM - 11:00 AMStorrs CampusAUST 344
Professor, Department of Biostatistics
University of North Carolina at Chapel Hill
Gillings School of Global Public Health
Statistical Learning Methods for Neuroimaging Data Analysis with Applications
The aim of this talk is to provide a comprehensive review of statistical challenges in neuroimaging data analysis from neuroimaging techniques to large-scale neuroimaging studies to statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate the four common themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four common themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
Friday, November 18, 2022
10:00 am ET, 1-hour duration
Coffee @ 9:30 in AUST 326
11/30Statistics Colloquium, Stefano Iacus
Statistics Colloquium, Stefano IacusWednesday, November 30th, 202204:00 PM - 05:00 PMStorrs CampusAUST 434
Dr. Stefano Iacus
Senior Research Scientist and Director of Data Science and Product Research
The Institute for Quantitative Social Science
Sentiment Analysis, Social Media and Subjective Well- Being
After reviewing a few basic concepts of sentiment analysis for social media analysis, we present the iSA algorithm which is an unbiased, statistically and computationally efficient method for the estimation of the aggregated distribution of opinions in a set of textual data. We discuss further the problems related to different types of bias that arise in the analysis of social media data and an attempt to control for it through statistical methods. Finally, we discuss an application of sentiment analysis that aims at extracting expressions of subjective well-being from Twitter data. In this application we show the impact of the recent COVID-19 pandemic on the derived well-being indicators.
Bio: Stefano M. Iacus is the Director of Data Science and Product Research at the Institute for Quantitative Social Science, Harvard University. He is working closely with the Dataverse and OpenDP projects and well as with the Data Science Services at IQSS. Iacus started his academic career at the University of Milan (Italy), where he became full professor of statistics in 2015. He founded and directed the Data Science Lab and two master courses in Finance and Economics and Data Science for Economics. In the period 2019-2022, he has also served as officer at the Joint Research Centre of the European Commission, where he led the team that explored the usage of non-traditional data sources in the context of evidence based policy making in migration and demography to support action during crisis periods and to refine preparedness measures. Since 2006, he has had a recurring visiting position at the Graduate School ot Mathematics at the University of Tokyo (Japan) and he co-leads the Yuima project. He has been a member of the R Core Team for the development of the R statistical environment from 1999 till 2014 and is now a member of the R Foundation for Statistical Computing. Iacus’ accomplishments extend beyond academia. During the COVID-19 pandemic, he managed a large-scale business-to-government project for the European Commission, producing insights for policy-making using data from mobile network operators covering most European Union member states. Iacus has published several books, many scientific articles, and a variety of open-source software products in a number of fields including causal inference, sentiment analysis, inference for stochastic processes, computational statistics, and quantitative finance. His work is widely cited across scholarly fields. He has founded two startup companies in the fields of social media analysis and quantitative finance.
Wednesday, November 30, 2022
4:00 pm ET, 1-hour duration
Coffee @ 3:30 in AUST 326