Date

2022-8-17

Author

Oltulu, Orçun

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

251
views

413
downloads

Cite This

Cost-efficient data collection and storage methods enable scientists, companies, and even regular computer users to reach high-dimensional data sets faster and cheaper. Even though personal computers are getting more powerful and efficient, some algorithms, tasks, and problems still require too much computational power and time to run on a personal computer. For a few decades, parallelization in statistical computing had an increasing trend, and researchers put significant effort into converting or adjusting known statistical methods and algorithms in parallel. The main reasons for the transition to parallel methods are the rapid growth in the size and the volume of data and the accelerated hardware developments. In this study, we applied the parallelization technique to statistical algorithms such as Linear Regression models, Non-parametric Regression models, and the measurement error kernel regression operator (MEKRO) algorithm for variable selection in Non-parametric Regression models. Simulation studies are conducted for each algorithm and recorded their accuracy measures and elapsed times to compare and see whether parallelization methods offer significant efficiency while maintaining the accuracy level as high as their sequential versions. The overall simulation results show that parallelization of the offers a great potential of time efficiency with negligible or no changes in accuracy values.

Subject Keywords

Parallel Computing, Multi-Core Systems, Linear Regression, Non- Parametric Regression, Variable Selection

URI

https://hdl.handle.net/11511/98772

Collections

Graduate School of Natural and Applied Sciences, Thesis