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Data Science Seminar Date: Friday, 14 February 2025 Time: 2:00 - 3:00 PM Venue: NKN Hall Subdata selection: Introduction and Recent Works Rakhi Singh IIT Madras. 14-02-25 Abstract 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 collected per day. We will begin by discussing an information-based method for subdata selection under the assumption that a linear regression model is adequate . A strength of this method, which is inspired by ideas from optimal design of experiments, is that it is superior to competing methods in terms of statistical performance and computational cost when the model is correct. A weakness of the method, shared with other model-based methods, is that it can give poor results if the model is incorrect. We will therefore conclude with a discussion of a model-free method. This talked is based on joint works with John Stufken at George Mason University, USA. Dr. Singh is an Assistant Professor in the Department of Mathematics at IIT Madras since January of this year. Prior to this, she was an Assistant Professor at State University of New York at Binghamton in the United States. She did her PhD in Mathematics (with specialization in Statistics) under a joint PhD program between Indian Institute of Technology Bombay and Monash University, Australia. She also did a postdoc for a couple of years at TU Dortmund and UNC Greensboro. Her primary research areas are design and analysis of experiments and subdata selection for high-dimensional data.
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