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IUPUI

1. Cheung, Chung Ching. A-Optimal Subsampling For Big Data General Estimating Equations.

Degree: 2019, IUPUI

Indiana University-Purdue University Indianapolis (IUPUI)

A significant hurdle for analyzing big data is the lack of effective technology and statistical inference methods. A popular approach for analyzing data with large sample is subsampling. Many subsampling probabilities have been introduced in literature (Ma, \emph{et al.}, 2015) for linear model. In this dissertation, we focus on generalized estimating equations (GEE) with big data and derive the asymptotic normality for the estimator without resampling and estimator with resampling. We also give the asymptotic representation of the bias of estimator without resampling and estimator with resampling. we show that bias becomes significant when the data is of high-dimensional. We also present a novel subsampling method called A-optimal which is derived by minimizing the trace of some dispersion matrices (Peng and Tan, 2018). We derive the asymptotic normality of the estimator based on A-optimal subsampling methods. We conduct extensive simulations on large sample data with high dimension to evaluate the performance of our proposed methods using MSE as a criterion. High dimensional data are further investigated and we show through simulations that minimizing the asymptotic variance does not imply minimizing the MSE as bias not negligible. We apply our proposed subsampling method to analyze a real data set, gas sensor data which has more than four millions data points. In both simulations and real data analysis, our A-optimal method outperform the traditional uniform subsampling method.

Advisors/Committee Members: Peng, Hanxiang, Rubchinsky, Leonid, Boukai, Benzion, Lin, Guang, Al Hasan, Mohammad.

Subjects/Keywords: Subsampling; Big Data; A-optimality; General Estimating Equations; High Dimensional Statistics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cheung, C. C. (2019). A-Optimal Subsampling For Big Data General Estimating Equations. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/20022

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Cheung, Chung Ching. “A-Optimal Subsampling For Big Data General Estimating Equations.” 2019. Thesis, IUPUI. Accessed August 24, 2019. http://hdl.handle.net/1805/20022.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Cheung, Chung Ching. “A-Optimal Subsampling For Big Data General Estimating Equations.” 2019. Web. 24 Aug 2019.

Vancouver:

Cheung CC. A-Optimal Subsampling For Big Data General Estimating Equations. [Internet] [Thesis]. IUPUI; 2019. [cited 2019 Aug 24]. Available from: http://hdl.handle.net/1805/20022.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cheung CC. A-Optimal Subsampling For Big Data General Estimating Equations. [Thesis]. IUPUI; 2019. Available from: http://hdl.handle.net/1805/20022

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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