Academy of Mathematics and Systems Science, CAS Colloquia & Seminars
Speaker:
Prof. Ming-Hui Chen,Department of Statistics, University of Connecticut
Inviter:
Xingbiao Hu
Title:
Online Updating of Statistical Inference in the Big Data Setting
Time & Venue:
2015.7.1 10:00-11:00am N202
Abstract:
We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residuals tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting. This is the joint work with Elizabeth D. Schifano, Jing Wu, Chun Wang, and Jun Yan of University of Connecticut.
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