Rothschild Lecture: From Small Data to Big Data and Back: Statistics and Data Science

1 hour 1 min, 233.27 MB, iPod Video 480x270, 29.97 fps, 44100 Hz, 522.11 kbits/sec

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Description:	Bickel, P (University of California, Berkeley) Wednesday 17th August 2016 - 16:00 to 17:00


Created:	2016-08-23 16:44
Collection:	Theoretical Foundations for Statistical Network Analysis
Publisher:	Isaac Newton Institute
Copyright:	Bickel, P
Language:	eng (English)


Abstract:	Modern statistics began with R.A.Fisher’s seminal work in the early twentieth century, with important predecessors such as Karl Pearson, contemporaries such as J.Neyman and successors such as A. Wald. The focus was on small data sets or summaries of larger ones. Analyses were based on simple models and given in terms of estimates, testing and confidence bounds. With the advent of big data, the size of datasets, their complexity and heterogeneity, and the lack of theory to build mechanistic probability models brought new issues to the fore. I will discuss some of these issues: 1. Computation 2. Prediction 3. Sparsity/dimension reduction4. Stability/robustness 5. Reduction to small data

Abstract:

Modern statistics began with R.A.Fisher’s seminal work in the early twentieth century, with important predecessors such as Karl Pearson, contemporaries such as J.Neyman and successors such as A. Wald. The focus was on small data sets or summaries of larger ones. Analyses were based on simple models and given in terms of estimates, testing and confidence bounds. With the advent of big data, the size of datasets, their complexity and heterogeneity, and the lack of theory to build mechanistic probability models brought new issues to the fore. I will discuss some of these issues: 1. Computation 2. Prediction 3. Sparsity/dimension reduction4. Stability/robustness 5. Reduction to small data

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