Estimation of Nonorthogonal Problem Using Time Series Dataset
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Abstract
Based on presentation of the principles of nonorthogonal problem, we discuss the difference of some approaches. A simple procedure to include the R-squared and Root Mean Square Error (R.M.S.E) is proposed and tested. The results showed that the Partial Least Square Regression provides better predictions due to a small R.M.S.E value.
Keyword. Nonorthogonal, Mean Square, Partial Least Square, R Square.
Table of Content
1.0 Introduction
2.0 The Ordinary Least Square Model
2.3 Ridge Regression (RR).
3.0 Result and Discussion
3.1 Illustrative Example
4.0 Conclusion
4.1 Recommendation
Reference
0
BARTHOLOMEW, D. (2018). Estimation of Nonorthogonal Problem Using Time Series Dataset. Afribary. Retrieved from https://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649
BARTHOLOMEW, DESMOND "Estimation of Nonorthogonal Problem Using Time Series Dataset" Afribary. Afribary, 29 Jan. 2018, https://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649. Accessed 10 Jun. 2023.
BARTHOLOMEW, DESMOND . "Estimation of Nonorthogonal Problem Using Time Series Dataset". Afribary, Afribary, 29 Jan. 2018. Web. 10 Jun. 2023. < https://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649 >.
BARTHOLOMEW, DESMOND . "Estimation of Nonorthogonal Problem Using Time Series Dataset" Afribary (2018). Accessed June 10, 2023. https://afribary.com/works/estimation-of-nonorthogonal-problem-using-time-series-dataset-3649