STATISTICAL PROCESS CONTROL OF AUTOCORRELATED DATA EXHIBITING GEOMETRIC BROWNIAN MOTION: ARITHMETIC RETURN MODEL APPROACH

Ayta, K. (2020). Control Charts for monitoring processes with Autocorrelated data. Masters Dissertation submitted to obtain the degree of, Master in Business Engineering: Data Analytics. Universitiet Gent.

CFI (2024). Durbin Watson Statistic. Corporate Finance Institute Resources. Retrieved on 29th February, 2024 from https://corporatefinanceinstitute.com/resources/data-science/durbin-watson-statistic/

Chajire, B. P., Dike, I. J., Abdulkadir S. S., and Torsen, E. (2024). Arithmetic Return model for removing autocorrelation from Statistical process control data exhibiting Geometric Brownian Motion. BIMA Journal of Science and Technology. 8(1B), 281-286. https://doi.org/10.56892/bima.v8i1.634

Chao-Wen, L., and Marion, R.R. (2001). Cusum Charts for Monitoring an Autocorrelated Process. Journal of Quality Technology. 33(3), 316- 334. https://doi.org/10.1080/00224065.2001.11980082 DOI: https://doi.org/10.1080/00224065.2001.11980082

Hussam, A., Mumen, R., Tarek, A., and Omar., B. (2021). A Guassian process approach for monitoring autocorrelated batch production processes. WILEY. https://doi.org/10.1002/qre.2951 DOI: https://doi.org/10.1002/qre.2951

Montgomery, D. C. (2013). Introduction to Statistical Quality Control (seventh edition). United Kingdom: John Wiley and Sons Inc.

Muhammad N. A., Garba, N. N., and Michael, A. O. (2023). Quality Control Analysis Of Diagnostic Radiology Equipment In 44 Nigerian Army Reference Hospital Kaduna, Kaduna State, Nigeria. FUDMA JOURNAL OF SCIENCES, 2(4), 61 - 66. Retrieved from https://fjs.fudutsinma.edu.ng/index.php/fjs/article/view/1319

Rupali, K. and Vikas, G. (2022). Estimation of the Change Point in the Mean Control Chart for Autocorrelated Processes. RT&A. 17(3), 69.

Salleh, R. M., Zawawi, N. I., Gan Z. F., and Nor, M. E. (2018). Autocorrelated process control: Geometric Brownian Motion approach versus Box-Jenkins approach. Conference Series: Journal of Physics. 995(012039), 1-11. https://doi.org/10.1088/1742-6596/995/1/012039 DOI: https://doi.org/10.1088/1742-6596/995/1/012039

Siaw, L. L., and Djauhari, M. A. (2013). Monitoring Autocorrelated Processes: A Geometric Brownian motion process approach. https://www.researchgate.net/publication/260829536 https://doi.org/10.1063/1.4823976 DOI: https://doi.org/10.1063/1.4823976

Siaw, L. L., Djauhari, M. A., and Mohamad, I. (2015). Modeling Autocorrelated Process Control with Industrial Application. https://www.researchgate.net/publication/282928394 https://doi.org/10.1109/IEEM.2014.7058614 DOI: https://doi.org/10.1109/IEEM.2014.7058614

Siaw, L. L., Chin, Y. L., Chee, K. C., and Li. L. V. (2022). Geometric Brownian Motion-Based Time Series Modeling Methodology for Statistical Autocorrelated Process Control: Logarithmic Return Model. International journal of Mathematics and Mathematical Sciences. https://doi.org/10.1155/2022/4783090 DOI: https://doi.org/10.1155/2022/4783090

Tim, S. (2023). Autocorrelation: What it is, how it works, Tests. Investopedia. Retrieved on 14th August 2023 from https://www.investopedia.com/terms/a/autocorrelation.asp

Zach (2021) The Durbin-Watson Test: Definition and example. STATOLOGY. Retrieved on 9th May, 2023 from https://www.statology.org/durbin-watson-test