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Implementation of Cumulative Probability Models for Big Data

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, Master of Sciences, Case Western Reserve University, EECS - Computer and Information Sciences.
Cumulative probability models have been introduced as a flexible alternative to linear models. However, they are computationally heavy especially when the number of distinct outcomes is large. We introduced and implemented three methods to address this problem. In the divide-and-combine method, the data are partitioned into subsets and the models are fit in parallel; in the binning and rounding methods, the outcomes are either grouped into bins or rounded (to certain significant digits or decimal places) before the models are fit. We implemented these approaches, built simulation programs, and developed an R package. We describe various challenges during the implementation and present their solutions. Examples include using ExitStack for data partition, and implementing an algorithm to merge all step functions to obtain the final estimates. We also briefly describe the results from our paper and an application to a large dataset.
Jing Li, Dr. (Committee Chair)
Chun Li, Dr. (Advisor)
Shuai Xu, Dr. (Committee Member)
59 p.

Recommended Citations

Citations

  • Chen, G. (n.d.). Implementation of Cumulative Probability Models for Big Data [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1619624862283514

    APA Style (7th edition)

  • Chen, Guo. Implementation of Cumulative Probability Models for Big Data. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1619624862283514.

    MLA Style (8th edition)

  • Chen, Guo. "Implementation of Cumulative Probability Models for Big Data." Master's thesis, Case Western Reserve University. Accessed APRIL 27, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case1619624862283514

    Chicago Manual of Style (17th edition)