|Title||Online Updating of Statistical Inference in the Big Data Setting.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Schifano, Elizabeth D., Jing Wu, Chun Wang, Jun Yan, and Ming-Hui Chen|
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 residual 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.
|Original Publication||Online updating of statistical inference in the big data setting.|
|PubMed Central ID||PMC5179229|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 GM070335 / GM / NIGMS NIH HHS / United States
Online Updating of Statistical Inference in the Big Data Setting.