Title

Do We Necessarily Have More Cost-Effective Information with Bigger Data?

Abstract

It is a normally held belief that more data provide more information. Such a feeling is widespread under big data movement, but largely without mentioning what kind of information one may need or be willing to utilize. In order to hold a reasonable discourse, I begin by exploring under certain stochastic models of one kind or another and then try to grasp what additional useful information may or may not entail as more data came in.

I will illustrate situations where a sentiment that more data may amount to more useful information is nearly valid. But, I will also show that the same belief may not be entirely justified across the board. That is, any relative gain in information may not be substantial as one utilizes more available data. Indeed, the relative gain in information may go down with more data.

In this presentation, I will share some preliminary ideas and analysis to highlight my thoughts. It is my earnest belief that there must be more to it than just using increased amount of available data simply because bigger datasets are out there!   

Biography

Prof. Nitis Mukhopadhyay received PhD degree (1975) from Indian Statistical Institute-Calcutta. He has been a full professor in the Department of Statistics, University of Connecticut-Storrs, USA since 1985. He served as the Head of this department from 1987-1990.

Prior to joining the University of Connecticut, Prof. Mukhopadhyay was a faculty member at the following institutions: Monash University-Melbourne, Australia (1976-77), University of Minnesota-Minneapolis (1977-78), University of Missouri-Columbia (1978-79), and Oklahoma State University-Stillwater (1979-85).

He made prolific contributions in a number of areas including statistical inference-parametric and nonparametric, sequential analysis, multiple comparisons, clinical trials, applied probability, econometrics, and applications. Prof. Mukhopadhyay is especially recognized for path-breaking contributions in (i) sequential analysis as well as (ii) selection and ranking.  His honors include elected Fellows of the Institute of Mathematical Statistics (2002), the American Statistical Association (2003), the American Association for the Advancement of Science (2012), elected Member of the International Statistical Institute (2007), elected Member of Connecticut Academy of Arts and Sciences (2014), Fellow of the Royal Statistical Society, the Abraham Wald Prize in Sequential Analysis (2008), the Don Owen Award (2015).

He was awarded the Honorary Fellowship by the Institute of Applied Statistics Sri Lanka (2017). He has been the Editor-in-Chief for the premier journal, Sequential Analysis, since 2004 and serves as an Associate Editor for a number of other leading international journals. 

As an author or co-author, Prof. Mukhopadhyay published 6 books, 18 book chapters, nearly 300 peer-reviewed research papers, and edited or co-edited more than 7 special volumes. He has supervised 26 Ph.D. students as a major adviser and has 4 Ph.D. continuing under his guidance at this time. His former PhD advisees have gone to academia, businesses, and industries to make their own marks.